@article{seifrid_lo_choi_tom_le_li_sankar_vuong_wakidi_yi_et al._2024, title={Beyond Molecular Structure: Critically Assessing Machine Learning for Designing Organic Photovoltaic Materials and Devices}, url={https://doi.org/10.26434/chemrxiv-2024-d20px}, DOI={10.26434/chemrxiv-2024-d20px}, abstractNote={Our study explores the current state of machine learning (ML) as applied to predicting and designing organic photovoltaic (OPV) devices. We outline key considerations for selecting the method of encoding a molecular structure and selecting the algorithm while also emphasizing important aspects of training and rigorously evaluating ML models. This work presents the first dataset of OPV device fabrication data mined from the literature. The top models achieve state-of-the-art predictive performance. In particular, we identify an algorithm that is used less frequently, but may be particularly well suited to similar datasets. However, predictive performance remains modest (R2 ≅ 0.6) overall. An in-depth analysis of the dataset attributes this limitation to challenges relating to the size of the dataset, as well as data quality and sparsity. These aspects are directly tied to difficulties imposed by current reporting and publication practices. Advocating for standardized reporting of OPV device fabrication data reporting in publications emerges as crucial to streamline literature mining and foster ML adoption. This comprehensive investigation emphasizes the critical role of both data quantity and quality, and highlights the need for collective efforts to unlock ML's potential to drive advancements in OPV.}, author={Seifrid, Martin and Lo, Stanley and Choi, Dylan and Tom, Gary and Le, My Linh and Li, Kunyu and Sankar, Rahul and Vuong, Hoai-Thanh and Wakidi, Hiba and Yi, Ahra and et al.}, year={2024}, month={Mar} }
 @article{seifrid_lo_choi_tom_le_li_sankar_vuong_wakidi_yi_et al._2024, title={Beyond Molecular Structure: Critically Assessing Machine Learning for Designing Organic Photovoltaic Materials and Devices}, url={https://doi.org/10.26434/chemrxiv-2024-d20px-v2}, DOI={10.26434/chemrxiv-2024-d20px-v2}, abstractNote={Our study explores the current state of machine learning (ML) as applied to predicting and designing organic photovoltaic (OPV) devices. We outline key considerations for selecting the method of encoding a molecular structure and selecting the algorithm while also emphasizing important aspects of training and rigorously evaluating ML models. This work presents the first dataset of OPV device fabrication data mined from the literature. The top models achieve state-of-the-art predictive performance. In particular, we identify an algorithm that is used less frequently, but may be particularly well suited to similar datasets. However, predictive performance remains modest (R2 ≅ 0.6) overall. An in-depth analysis of the dataset attributes this limitation to challenges relating to the size of the dataset, as well as data quality and sparsity. These aspects are directly tied to difficulties imposed by current reporting and publication practices. Advocating for standardized reporting of OPV device fabrication data reporting in publications emerges as crucial to streamline literature mining and foster ML adoption. This comprehensive investigation emphasizes the critical role of both data quantity and quality, and highlights the need for collective efforts to unlock ML's potential to drive advancements in OPV.}, author={Seifrid, Martin and Lo, Stanley and Choi, Dylan G. and Tom, Gary and Le, My Linh and Li, Kunyu and Sankar, Rahul and Vuong, Hoai-Thanh and Wakidi, Hiba and Yi, Ahra and et al.}, year={2024}, month={Apr} }
 @article{seifrid_lo_choi_tom_le_li_sankar_vuong_wakidi_yi_et al._2024, title={Beyond molecular structure: critically assessing machine learning for designing organic photovoltaic materials and devices}, volume={5}, ISSN={["2050-7496"]}, url={https://doi.org/10.1039/D4TA01942C}, DOI={10.1039/D4TA01942C}, abstractNote={Our study explores the current state of machine learning (ML) as applied to predicting and designing organic photovoltaic (OPV) devices. We outline key considerations for selecting the method of encoding...}, journal={JOURNAL OF MATERIALS CHEMISTRY A}, author={Seifrid, Martin and Lo, Stanley and Choi, Dylan G. and Tom, Gary and Le, My Linh and Li, Kunyu and Sankar, Rahul and Vuong, Hoai-Thanh and Wakidi, Hiba and Yi, Ahra and et al.}, year={2024}, month={May} }
 @article{seifrid_strieth-kalthoff_haddadnia_wu_alca_bodo_arellano-rubach_yoshikawa_skreta_keunen_et al._2024, title={Chemspyd: An Open-Source Python Interface for Chemspeed Robotic Chemistry and Materials Platforms}, url={https://doi.org/10.26434/chemrxiv-2024-33sfl}, DOI={10.26434/chemrxiv-2024-33sfl}, abstractNote={We introduce Chemspyd, a lightweight, open-source Python package for operating the popular laboratory robotic platforms from Chemspeed Technologies. As an add-on to the existing proprietary software suite, Chemspyd enables dynamic communication with the automated platform, laying the foundation for its modular integration into customizable, higher-level laboratory workflows. We show the applicability of Chemspyd in a set of case studies from chemistry and materials science. We demonstrate how the package can be used with large language models to provide a natural language interface. By providing an open-source software interface for a commercial robotic platform, we hope to inspire the development of open interfaces that facilitate the flexible, adaptive integration of existing laboratory equipment into automated laboratories.}, author={Seifrid, Martin and Strieth-Kalthoff, Felix and Haddadnia, Mohammad and Wu, Tony and Alca, Emre and Bodo, Leticia and Arellano-Rubach, Sebastian and Yoshikawa, Naruki and Skreta, Marta and Keunen, Rachel and et al.}, year={2024}, month={Feb} }
 @article{seifrid_strieth-kalthoff_haddadnia_wu_alca_bodo_arellano-rubach_yoshikawa_skreta_keunen_et al._2024, title={Chemspyd: An Open-Source Python Interface for Chemspeed Robotic Chemistry and Materials Platforms}, url={https://doi.org/10.26434/chemrxiv-2024-33sfl-v2}, DOI={10.26434/chemrxiv-2024-33sfl-v2}, abstractNote={We introduce Chemspyd, a lightweight, open-source Python package for operating the popular laboratory robotic platforms from Chemspeed Technologies. As an add-on to the existing proprietary software suite, Chemspyd enables dynamic communication with the automated platform, laying the foundation for its modular integration into customizable, higher-level laboratory workflows. We show the applicability of Chemspyd in a set of case studies from chemistry and materials science. We demonstrate how the package can be used with large language models to provide a natural language interface. By providing an open-source software interface for a commercial robotic platform, we hope to inspire the development of open interfaces that facilitate the flexible, adaptive integration of existing laboratory equipment into automated laboratories.}, author={Seifrid, Martin and Strieth-Kalthoff, Felix and Haddadnia, Mohammad and Wu, Tony and Alca, Emre and Bodo, Leticia and Arellano-Rubach, Sebastian and Yoshikawa, Naruki and Skreta, Marta and Keunen, Rachel and et al.}, year={2024}, month={Feb} }
 @article{seifrid_strieth-kalthoff_haddadnia_wu_alca_bodo_arellano-rubach_yoshikawa_skreta_keunen_et al._2024, title={Chemspyd: an open-source python interface for Chemspeed robotic chemistry and materials platforms}, url={https://doi.org/10.1039/D4DD00046C}, DOI={10.1039/D4DD00046C}, abstractNote={We introduce Chemspyd , a lightweight, open-source Python package for operating the popular laboratory robotic platforms from Chemspeed Technologies.}, journal={Digital Discovery}, author={Seifrid, Martin and Strieth-Kalthoff, Felix and Haddadnia, Mohammad and Wu, Tony C. and Alca, Emre and Bodo, Leticia and Arellano-Rubach, Sebastian and Yoshikawa, Naruki and Skreta, Marta and Keunen, Rachel and et al.}, year={2024} }
 @article{strieth-kalthoff_hao_rathore_derasp_gaudin_angello_seifrid_trushina_guy_liu_et al._2024, title={Delocalized, asynchronous, closed-loop discovery of organic laser emitters}, url={https://doi.org/10.1126/science.adk9227}, DOI={10.1126/science.adk9227}, abstractNote={Contemporary materials discovery requires intricate sequences of synthesis, formulation, and characterization that often span multiple locations with specialized expertise or instrumentation. To accelerate these workflows, we present a cloud-based strategy that enabled delocalized and asynchronous design-make-test-analyze cycles. We showcased this approach through the exploration of molecular gain materials for organic solid-state lasers as a frontier application in molecular optoelectronics. Distributed robotic synthesis and in-line property characterization, orchestrated by a cloud-based artificial intelligence experiment planner, resulted in the discovery of 21 new state-of-the-art materials. Gram-scale synthesis ultimately allowed for the verification of best-in-class stimulated emission in a thin-film device. Demonstrating the asynchronous integration of five laboratories across the globe, this workflow provides a blueprint for delocalizing-and democratizing-scientific discovery.}, journal={Science}, author={Strieth-Kalthoff, Felix and Hao, Han and Rathore, Vandana and Derasp, Joshua and Gaudin, Théophile and Angello, Nicholas H. and Seifrid, Martin and Trushina, Ekaterina and Guy, Mason and Liu, Junliang and et al.}, year={2024}, month={May} }
 @article{seifrid_karki_wakidi_vezin_welton_bazan_chmelka_nguyen_reddy_2024, title={Importance of Short-Range Order in Governing Thin Film Morphology and Electronic Properties of Polymeric Organic Semiconductors}, volume={1}, ISSN={["1520-5002"]}, url={https://doi.org/10.1021/acs.chemmater.3c01931}, DOI={10.1021/acs.chemmater.3c01931}, abstractNote={Semiconducting polymers provide a ubiquitous platform for a range of applications in molecular electronics and photovoltaics, but the ordered and disordered regions of these materials impart different optoelectronic properties. By resolving local morphology using solid-state magnetic resonance spectroscopy and modeling techniques, here, we demonstrate that the PTB7-Th donor–acceptor (D–A) copolymer and P3HT and MEH-PPV homopolymers exhibit different degrees of the short-range order, which can be associated with the large differences in their charge carrier mobilities. The high degree of local order in PTB7-Th (84–99%) is facilitated by noncovalent interactions between D and A moieties. In contrast to this, the reduced local order in P3HT (30–44%) and MEH-PPV (39–43%) homopolymers is due to the distortions in the vicinities of backbone and side chain moieties that lead to conformationally tilted polymer chains. Combined solid-state NMR and density functional theory (DFT) modeling allows the degree of backbone torsion in these materials to be determined, and insights into packing interactions are obtained by two-dimensional (2D) 1H–1H, 1H–13C, and 1H–19F correlation NMR spectroscopy. In addition, the different paramagnetic species and hyperfine interactions are analyzed by EPR spectroscopy and are expected to influence the charge carrier mobilities. A detailed analysis of the local structures presented in this study helps explain the morphological anomalies and their impact on bulk charge carrier mobilities and electronic density of states, thus providing essential insights into the morphology–property relationships in polymeric organic semiconductors.}, journal={CHEMISTRY OF MATERIALS}, author={Seifrid, Martin and Karki, Akchheta and Wakidi, Hiba and Vezin, Herve and Welton, Claire and Bazan, Guillermo C. and Chmelka, Bradley F. and Nguyen, Thuc-Quyen and Reddy, G. N. Manjunatha}, year={2024}, month={Jan} }
 @article{a materials acceleration platform for organic laser discovery_2023, url={https://publons.com/wos-op/publon/57229483/}, DOI={10.1002/ADMA.202207070}, abstractNote={Abstract}, journal={Advanced Materials}, year={2023} }
 @article{lo_seifrid_gaudin_aspuru-guzik_2023, title={Augmenting Polymer Datasets by Iterative Rearrangement}, url={https://doi.org/10.1021/acs.jcim.3c00144}, DOI={10.1021/acs.jcim.3c00144}, abstractNote={One of the biggest obstacles to successful polymer property prediction is an effective representation that accurately captures the sequence of repeat units in a polymer. Motivated by the success of data augmentation in computer vision and natural language processing, we explore augmenting polymer data by iteratively rearranging the molecular representation while preserving the correct connectivity, revealing additional substructural information that is not present in a single representation. We evaluate the effects of this technique on the performance of machine learning models trained on three polymer datasets and compare them to common molecular representations. Data augmentation does not yield significant improvements in machine learning property prediction performance compared to equivalent (non-augmented) representations. In datasets where the target property is primarily influenced by the polymer sequence rather than experimental parameters, this data augmentation technique provides molecular embedding with more information to improve property prediction accuracy.}, journal={Journal of Chemical Information and Modeling}, author={Lo, Stanley and Seifrid, Martin and Gaudin, Théophile and Aspuru-Guzik, Alán}, year={2023}, month={Jul} }
 @article{allen_bediako_bowman_calabrese_caretta_cersonsky_chen_correa_davidson_dresselhaus-marais_et al._2023, title={Opinion 35+1 challenges in materials science being tackled by PIs under 35(ish) in 2023}, volume={6}, ISSN={["2590-2385"]}, url={https://publons.com/wos-op/publon/62772428/}, DOI={10.1016/j.matt.2023.06.046}, abstractNote={Here we highlight 35 (+1) global researchers approximately under the age of 35. The annual cohort was self-generated by initial seed invitations sent by the editorial team, with each contributor inviting the next in a self-selecting unrestricted (nominally supervised) manner. The final collection is an inspiring look at the challenges the current generation of materials researchers are tackling, demonstrating the interdisciplinarity of materials science. Here we highlight 35 (+1) global researchers approximately under the age of 35. The annual cohort was self-generated by initial seed invitations sent by the editorial team, with each contributor inviting the next in a self-selecting unrestricted (nominally supervised) manner. The final collection is an inspiring look at the challenges the current generation of materials researchers are tackling, demonstrating the interdisciplinarity of materials science. In our December 2021 issue, we published our first “35 under 35” article.1Aguado B. Bray L.J. Caneva S. Correa-Baena J.P. Di Martino G. Fang C. Fang Y. Gehring P. Grosso G. Gu X. et al.35 challenges in materials science being tackled by PIs under 35(ish) in 2021.Matter. 2021; 4: 3804-3810https://doi.org/10.1016/j.matt.2021.11.003Abstract Full Text Full Text PDF Scopus (1) Google Scholar We weren’t the first to come up with the concept of highlighting the “next generation” of rising stars. Consider Forbes 30 Under 30 or Fortune’s 40 Under 40 (which predates Forbes). In academia, the concept was adopted and adapted, resulting in annual announcements of the MIT Technology Review’s Innovators Under 35 list as well as American Chemical Society’s C&EN Talented Twelve.2Halford B. Torrice M. C&EN’S Talented Twelve.Chemical and Engineering News. 2022; 100https://cen.acs.org/people/CENs-Talented-12/100/i25Google Scholar Why do we like to highlight the next generation? I believe the benefits are threefold. First, the next generation of any profession (be they tech entrepreneurs or academics) are a mysterious bunch, comprised of innovators, thought leaders, and disrupters, pushing the limits of a field. We don’t know anything about them. Unlike the established status quo (who we all know) rehashing the same story (for better or worse), the next generation inspires new directions and new ideas. Second, more pragmatically for Matter, we get to interact with a new group of authors which we likely didn’t interact with before. Particularly at the assistant professor level, it may be a year (or so) until one can produce that first “great” paper for submission to Matter (or any other high-quality journal). Collecting these contributions results in a first introduction (indeed, many of our 2021 contributors have become authors since!). Finally, as recognized by business and academia alike, “being young” has certain advantages in many fields. In addition to youthful exuberance, confidence, and energy, the emerging generation can take more risks, and re-invent or create a field. They are at the cutting edge … and they are more likely to reply to editorial emails. To deviate from the existing lists, which are typically compiled via nominations and selection criteria, we decided to implement a self-generating list of invitees. The process was similar to our prior effort:1Aguado B. Bray L.J. Caneva S. Correa-Baena J.P. Di Martino G. Fang C. Fang Y. Gehring P. Grosso G. Gu X. et al.35 challenges in materials science being tackled by PIs under 35(ish) in 2021.Matter. 2021; 4: 3804-3810https://doi.org/10.1016/j.matt.2021.11.003Abstract Full Text Full Text PDF Scopus (1) Google Scholar the editorial team at Matter sent an initial three invitations to three individuals to contribute both a brief description of their own research and a recommendation of the next researcher to invite (see Figure 1). These three initial invitations were sent to three global regions in three distinct fields, to attempt to limit any overlap, as well as to reflect a more diverse community (the process would be slightly quicker with three concurrent and parallel invitation chains). The end result was 36 individuals across 33 institutions (Northwestern, Cal, and University of Delaware each have two) in 7 countries, totalling 20 males and 16 females. The primary motivation behind this self-generating list was to avoid our own selection bias. Rather than cover the same cohort that is typically highlighted by other lists based on accomplishments and recognition by the “older” generation, we let the chips fall and trusted the self-selected output. Who else would be better to suggest emerging researchers than their own peers? The result, of course, is that the inclusion on this list is not overtly an award or accolade, but rather a selection to represent the emerging generation of materials scientists. Indeed, ranking careers at their onset is a near-impossible task. So, while this list is not the “top 35,” this is by design … but the individuals included are at the very least at the top of the minds of their peers. Our intent is a snapshot of contemporaries, reflecting the global variety of exciting materials science, applications, and directions. And perhaps a few Matter submissions in the near future. It behooves us to note that we initially intended this group to be published at the end of 2022. However, the team at Matter has endured some editorial changes, and the article was sidelined for a few months before renewed efforts in the spring of 2023, from which we attained the remaining necessary contributions (the three chains terminated within the same week, so we went with a nominally extended total of 36). After a final round of bookkeeping, collating, and formatting, the resulting 36 challenges (in alphabetical order) are: My research group builds and utilizes novel scanning probe microscopy techniques to visualize electronic behavior in quantum materials, with particular focus on two-dimensional materials, topological states of matter, and correlated electron systems. To this end, we are constructing a new microwave impedance microscope integrated into a dilution refrigerator, which will open up the capacity to image the real-space conductivity profile of quantum electronic states at low temperatures (down to 50 mK). This imaging method is ideally suited for shedding light on correlated insulator states, edge plasmons, and topological modes that propagate along domain walls and crystal boundaries. It is imperative to meet the challenge of escalating global energy demand with the innovation of unprecedentedly efficient renewable energy conversion/storage systems as well as information and communication technologies that operate at orders of magnitude lower energy consumption. The Bediako Lab designs and synthesizes atomically thin precisely tailored two-dimensional materials in which the collective behavior of electrons can be studied and controlled. We leverage these materials to uncover the principles that underlie efficient manipulation of electron transport within solids—the basis for ultralow-power electronic devices—and across solid-liquid interfaces—enabling the next-generation of fuel cells and electrolyzers for energy conversion. Our lab applies and develops advanced scanning and transmission electron microscopy methods to study electroceramics and nanomaterials. We work to understand, design, and synthesize the nanoscale structures, chemistries, and defects governing their behavior during energy conversion and storage, as well as carbon capture and utilization. For example, atomic-resolution imaging and spectroscopy of interfaces within and between ion-conducting ceramics elucidates mass and charge transport processes in solid oxide fuel/electrolysis cells and solid-state batteries. Additionally, in situ microscopy reveals reaction dynamics governing the capture and release of carbon dioxide by earth-abundant sorbents used to remove emissions from the atmosphere and exhaust gases. My lab develops new instruments, sample environments, and analyses in rheology and neutron/X-ray scattering to address a range of fundamental and applied problems in polymer and soft materials engineering. Our goal is to understand the microstructural basis of complex flow phenomena and subsequent performance in materials including polymers, proteins, and colloids; this approach then enables design of new materials with improved mechanical properties and flow stability. We are currently using this approach in several areas: injectable medications and drug delivery, field-directed assembly of polymeric materials, flow instabilities in self-assembled fluids, sprayable coatings, and sustainable rheological modifiers. Modern technologies require materials with dynamic and tunable functional properties. For example, the on-demand control of the electrical, optical, and magnetic properties of thin film materials is instrumental for next-generation memory and logic, sensing, quantum computing, and even energy conversion. Oxide materials are as rich in their physical phenomena and potential functionalities as they are ubiquitous in nature, while also providing a powerful playground for innovation. My research focuses on the atomic-scale synthesis and in situ characterization of designer oxide thin films with the goal of manipulating their functional properties. Many of the most interesting problems—including those related to optical circuitry, batteries, drug delivery, and gas storage—constitute multi-component, often complex, chemical systems that are difficult and costly to model computationally. Thus, it is even more crucial to extract any information within the computed data, often by leveraging tools of data science and machine learning (ML). My group develops physics-inspired representations to encode such "messy" systems numerically, expanding successful practices from the atomistic modeling community to include aspects of anisotropy and hierarchy. This approach enables us to use simpler and more interpretable ML models, opening previous black boxes toward future understanding. The increasing demand for structural materials serving under extreme environments calls for the development of emerging metal alloys with increasingly complex compositions. However, processing of complex alloys via traditional routes (e.g., casting) is challenging. Additive manufacturing is a disruptive technology for creating materials and components in a single print. My group focuses on development of new materials with engineered structures and outstanding mechanical properties by harnessing the vast compositional design space of complex alloys and the far-from-equilibrium processing signature of metal additive manufacturing. Meanwhile, we develop high-throughput additive manufacturing techniques to accelerate the pace of compositionally complex alloy discovery. The immune system is broadly implicated in human health—it is involved in infectious disease, cancer, aging, and a stunning array of auto-immune disorders ranging from arthritis to neurodegeneration and chronic pain. To leverage the immune system and overcome these diseases, we require tools that allow us to study and ultimately reprogram the immune system. My group is approaching this grand challenge by applying concepts from nanomedicine to design nanostructured scaffolds that can communicate more coherently with biological matter. Our approach uses biomaterials that self-assemble from bioinspired multifunctional nanoparticles to mimic the complex interactions that govern the immune system. My group aims to overcome constraints of typical design and synthesis in materials discovery by bridging knowledge across disciplines to enable precise structuring of matter across length scales. Our efforts include coupling principles of crystal growth and nanomaterial design with electrochemical additive manufacturing, leveraging scanning electrochemical probes to spatially confine deposition. By tuning crystallographic structure and composition across three dimensions, we aim to design functional materials such as battery electrodes with defined crystallographic orientation, plasmonic arrays, and surfaces with spatially controlled wettability. Across these efforts we integrate data-driven approaches into traditional synthesis workflows to enable deterministic navigation of design spaces. My group develops X-ray and visible-light microscopes with computer-vision tools to quantify the science that can enable sustainable manufacturing. Using these tools, we can quantifies how nanoscopic defect structures inside macroscopic materials dictate their performance—in metals processing, in material properties, in extracting metals from ores, and in 3D printing technology. We recently used this approach to study decarbonization in steelmaking (8% of global CO2) by changing from coal to hydrogen in the “ironmaking” reactors that reduce iron ores into molten iron. We showed how the nanoscopic grains native to the process create alternate phases and sintering that cause reactors to fail. To decarbonize critical technologies like steelmaking requires insights like these for reactor design and optimization. Our generation is faced with the formidable challenge of addressing energy usage and production to simultaneously meet increasing global energy demands and mitigate anthropogenic climate change. Because the efficiency of every optoelectronic device is dictated by the angular distribution of light and energy, the goal of my group’s research is to elucidate the interplay of surface chemistry and directional photophysics in nanomaterials. Our fundamental studies in time-resolved momentum spectroscopy allow us both to understand why certain nanocrystals display extraordinary optical phenomena and to design nanomaterials optimized for ultra-high-efficiency LEDs, solar cells, and next-generation quantum computing and communication. Electron transfer is the basis of the most fundamental cellular processes, ranging from photosynthesis to cellular respiration. Over billions of years of evolution, these natural systems have acquired critical advantages that engineered systems have yet to replicate. My lab seeks to understand these fundamental biological electron transfer processes to improve engineered systems. Specifically, we develop and deploy equitable technologies for human health, environmental remediation, and sustainability. Global inequality is the highest in recent history, and disenfranchised groups are disproportionately burdened by pollution while receiving little benefit from the industrial infrastructure causing it. Inequities are exacerbated by limited access to healthcare, contributing to lower life expectancies and preventable deaths. My group seeks to address these inequalities by developing materials inspired by biological electron transfer. We are interested in investigating the microscopic origin of the macroscopic behavior of various polymer and soft matter materials. A combination of molecular simulations and theory is employed in our research activities. Topics currently investigated include (1) the effects of polymer topology on the thermodynamics, rheology, and mechanics of polymeric materials, (2) the transport of nanoscale objects in complex polymeric environments, and (3) the scale-bridging physics in the large deformation and fracture behavior of thermoplastics, elastomers, and gels. My group seeks to assess and harness the bioinstructive properties of glycosaminoglycans (GAGs) for biomaterial applications. Individual GAG saccharides can undergo modifications at various positions via the action of enzymes, rather than template control. As a result, GAGs are highly complex and play diverse roles in many biological processes. However, their analysis is particularly challenging. I seek to analytically capture the full complexity of GAGs within biomaterial and tissue samples in a rapid, high-sensitivity, and spatially resolved manner utilizing mass spectrometry imaging together with multivariate analysis, while exploring the complex biological-material interactions using high-throughput screening methodologies. Topological superconductors (Tsc) are exotic quantum matters that transmit current with zero resistance and host a special type of excitation called “Majorana modes” on the boundaries. Since Majorana modes could exhibit non-abelian braiding statistics, Tsc are considered candidate platforms for performing topological quantum computation. However, despite the extensive efforts made over the past decades, unambiguously confirmed Tsc materials remain extremely rare. My group currently focuses on accelerating the material discovery for Tsc by (1) theoretically deriving guiding principles for material predictions from fundamental understandings of Tsc and (2) predicting new experimental signatures that can be used to identify Tsc materials. Understanding the fundamental physics behind emergent phenomena, such as high-temperature superconductivity and quantum spin liquid, is a significant challenge in condensed matter physics with profound implications for energy science and quantum information science. My research group is committed to developing cutting-edge numerical algorithms that advance theoretical modeling of quantum materials. Those algorithms empower us to unravel the intricate nature of these emergent phenomena. In particular, by integrating AI techniques with numerical simulations, we hope to bridge the gap between fundamental theory, materials synthesis, and characterization. Ultimately, our collective vision is to chart a path toward designing advanced quantum materials with exceptional electronic and magnetic properties. Controllable doping of organic semiconductors is essential for the development of optoelectronic devices such as perovskite or organic solar cells. Conventional doping by the addition of impurities can modify their electronic properties. However, the stability and doping efficiency remain conspicuously low, which threatens the commercialization of these devices. My group strives to develop alternative doping strategies for organic semiconductors, which allow for the printing of stable thin films with good optoelectronic properties. Particularly, we are now designing new chemical reactions to pre-dope organic semiconductors, hoping to provide approaches for fabricating more efficient and cheap solar cells. The protection of spin lifetime from the effect of solid-state vibrations is a crucial challenge in quantum science, magnetic resonance, spintronics, and many other fields. In my group, we combine advanced electronic structure theory with open quantum system theory to predict spin-phonon relaxation in realistic materials and provide a detailed picture of its origin. We envision that a further combination of ab initio spin dynamics with machine-learning methods and high-throughput simulations will make it possible to design new solid-state magnetic materials with application-tailored properties. Modern medicine relies on the molecular integrity of clinical samples to diagnose disease and therapeutics for their treatment. Nowhere is this situation better illustrated than the COVID-19 vaccines that have changed the landscape of the pandemic, yet these and other biologics suffer from temperature sensitivity that preclude long-term storage at ambient temperatures. We seek to create functional three-dimensional polymer-based materials platforms to enhance the stability of biologic therapeutics and clinical specimens with long-term goals to discover universal stabilizers that can be applied across distinct sample types such as antibody therapeutics, vaccines, and mammalian cell lines. The Moreno-Hernandez Laboratory utilizes liquid phase transmission electron microscopy techniques to understand the nanoscale structural dynamics of electrochemical materials in realistic environments. Our goal is to understand how fundamental atomic-scale interactions culminate as continuum-scale properties such as device efficiency and stability. We synthesize nanocrystals with meticulous control of nanoscale features and precisely monitor their structural dynamics at near-atomic resolution during electrochemical transformations. We envision that this information will be critical for the design of next-generation electrochemical materials that meet the challenges of the renewable energy sector. Our society is faced with a pressing need for technologies that mitigate dependence on fossil fuels. Among these is the need to develop efficient, stable, and selective catalysts that convert CO2 into value-added chemicals using renewable and abundant inputs. Taking loose inspiration from biological systems, my research group synthesizes molecular and interfacial CO2 reduction electrocatalysts, with a particular emphasis on design of local reaction environments. Using a combination of electrochemical, acoustic, and spectroscopic techniques, we interrogate our catalysts under operating conditions. These experiments provide information about how properties of the catalyst and its environment influence kinetics, product selectivity, and reaction mechanisms. Electrochemical conversion of renewable energy to fuels such as hydrogen and high-value chemicals is critical to accelerate our transition to a net-zero society. Such technologies are currently limited by the activity, stability, and selectivity of catalysts employed in them. Rational design of next-generation catalysts relies on gaining molecular-level understanding of their active sites and reaction mechanisms. My group uses X-ray, vibrational, and optical spectroscopy to study complex, polarized catalyst-electrolyte interfaces under operating conditions to improve our understanding of these energy conversion processes and accelerate the discovery of new catalysts. Organic mixed ionic-electronic conductors are a class of materials with potential applications in a wide range of areas including healthcare, sensing, energy storage, and neuromorphic computing. These materials are relatively poorly understood. Achieving optimal performance requires carefully balancing order and disorder via molecular design, formulation, and processing. My research group operates at the intersection of materials science, machine learning (ML), and automation. Our aim is to catalyze a transformative shift in materials research through automated experiments guided by ML, known as self-driving labs (SDLs). We approach this challenge bottom-up by exploring self-assembly of sequence-defined conjugated polymers, and top-down by leveraging robots for complex material formulations and processing. Simultaneously, we develop and demonstrate the power of SDLs and data-driven research. Semiconductor nanocrystals show immense promise for thin-film device applications such as LEDs, tunable on-chip lasers, and photovoltaics. However, devices operating at high current densities introduce many-body interactions which can have an adverse effect on device performance. The primary aim of my group is to understand how variations in nanocrystal chemical, material, and surface properties impact these multiply excited and charged states. We leverage my expertise in photon counting and ultrafast spectroscopy to develop techniques which enable us to build comprehensive models of excited state processes within nanocrystal thin films. Global electrification has changed the world and the way we live. Now, it is time for the energy and chemical industry sectors to undergo deep electrification—the ambition that drives our research on chemical transformations directed by electric current. The electrochemical processes of key interest, for example water splitting and nitrogen reduction to ammonia, can be achieved, at high efficiencies, in model laboratory setups, as demonstrated in our own work. However, translation of these processes into the operational environment of practical devices, even on a small scale, produces new, sometimes unexpected, challenges in achieving the desired performance. Addressing this, in particular through the use of specialized cells, modeling tools, and in situ characterization under practically relevant conditions, is one major focal point of our current research. Separations are indispensable for solving global challenges, including waste recycling, energy and food security, and water purification. Stimuli-responsive materials are a powerful platform for selective separations. My group seeks to advance the molecular-level design of electro-responsive materials to achieve precise selectivity toward specific molecules. We have studied redox-active materials and integrated them into reactive separation systems for diverse applications, ranging from resource recovery to waste treatment. Our fundamental goal is to understand and control the equilibrium and dynamic behavior of these stimuli-responsive materials and to elucidate the underlying mechanisms for selective interactions. The production of chemicals is central to modern society but is also responsible for a significant fraction of anthropogenic emissions and remains one of the most difficult to decarbonize sectors. In the Swearer Lab, we develop materials that combine the latest insights in catalytic active site design with the engineered photonic nanostructures to capture and transfer radiant electromagnetic energy (i.e., light) to decarbonize chemical reactions of industrial and societal importance. We are developing various platforms that harness photonic energy, such as dilute plasmonic alloys and functionalized photocatalytic metasurfaces, that take advantage of the high-energy, out-of-equilibrium dynamics afforded by photoexcitation. Why are living or autonomous systems able to exhibit timescales much longer than those of the underlying components, e.g., in the circadian rhythm or in stable memories? My group investigates these questions and how the emergent behavior remains robust under perturbations and external cues. By developing new conceptual and analytical tools, we pinpoint how such dynamics arise that are necessary for system regulation, growth, and motility. Our work provides design principles for targeted dynamics in synthetic systems or in the engineering of reconfigurable materials, e.g., through dissipative self-assembly. Dwindling freshwater, along with the skyrocketing demand for critical minerals, highlight our need to efficiently separate ions from aqueous mixtures. Separating a desired ion from a mixture is a long-standing chemical problem because of the similarities in size, shape, charge density, and coordination geometry of many transition metals and rare earth elements. My research group uses porous materials, which can act like molecular sponges, to capture toxic contaminants or valuable minerals from water. By using simple building blocks to assemble dynamic materials with selective binding sites, our research pushes the boundaries of ion selectivity and capacity in solid-state adsorbents. My research program centers on understanding how to make electronics harmonious with the body and environment. Team Tran leverages the rich palette of polymer chemistry to design electronic materials encoded with information for self-assembly, degradability, and mechanical softness. We encode sequence specificity, labile chemical motifs, and architectures for emergent properties. Beyond the molecular design of the polymers, we are interested in understanding the processing parameters to achieve morphologies suited for high electronic performance as well as seamlessly integrating materials for device fabrication. My research focuses on identifying accurately what a molecule is and where it is. However, the information is mutually exclusive. This is needed to understand chemistry/biology at the nanoscale in various fields, including protein function within cells, nanoparticles functionalized for drug delivery, molecules in inkjet-printed 2D materials, and interfacial phenomena in OLEDs. We have been overcoming this at micro/nanoscale using advanced mass spectrometry imaging, OrbiSIMS. Our recent findings indicate we can measure not only mass/charge but also detect molecular structure. Challenges lie in understanding phenomena behind it to achieve structural detection, spatially resolved at nanoscale from small to large molecules such as peptides/proteins. In living organisms, intelligence emerges both cognitively and physically. We call living machines’ passive adaptability, capacity for morphological computation, and resilient body-environment interactions physical intelligence. Conversely, engineers build computationally intelligent machines. Robots today often require contrived environments to robustly perform, failing in real-world scenarios. To augment robots with physical intelligence, our team in the Robotic Matter Lab creates artificial muscles, sensors, and control strategies for soft, bioinspired machines. We engineer processing and manufacturing methods to create robotic materials that give robots distributed, energy-efficient, and controllable sensorimotor capabilities. From molecule to machine, we advance machine intelligence through materials design, creating deployable robots that will help us tackle myriad global challenges. Recent years have seen staggering advances in our ability to prepare, manipulate, and detect light fields in nanoscale structures and with non-classical (quantum) properties. Translation of these developments into materials science and chemistry holds the key to new ultra-sensitive optical spectroscopy, new materials for optical quantum technologies, and novel functionality in optoelectronics. Our group designs nanophotonic materials systems and develops suitable techniques in optical spectroscopy to establish how nanoscale fields and quantum light interact with electronic materials and biological analytes and how these interactions can be used in applications. Living cells and tissues exhibit highly unusual viscoelastic properties compared to synthetic materials. They are strong enough to withstand substantial mechanical loads. They also actively adjust the stiffness and remodel in response to the changing mechanical environment. Drawing inspirations from mechanobiology, our group works on designing synthetic polymers that emulate the mechano-sensing and adaptation of biological systems. We are interested in gaining insights on how sophisticated molecular designs can be used to achieve complex nonlinear mechanics in dynamic polymer systems, and thus creating new active materials. My group seeks to understand how to integrate chemical synthesis, materials science, and advanced manufacturing to design materials over multiple length scales. Currently, we are engaged in the following research directions: democratizing the manufacturing of advanced materials via the development of accessible chemistries and processing strategies for additive manufacturing; understanding how to use dynamic bonds with architected materials to create “living” materials that can grow and adapt to their environment; and integrating self-assembly and additive manufacturing to design hierarchical materials. In doing so, we hope to engineer advanced functional materials that can tackle societal challenges in healthcare, energy, and climate change. The development of electrocatalysts is key for renewable energy technologies and other important industrial processes. Metal-organic frameworks (MOFs) possess many “super genes” like periodic structure and controllable components for both high-performance electrocatalyst design and fundamental mechanism study. However, their low electron transfer capacity, poor stability, and metal inaccessibility are challenging for their application in electrocatalysis. My research aimed at construction of novel MOF-based electrocatalysts by developing effective design strategies like coordination regulation, surface functionalization, and defect engineering. Also, we established strategies that combine operando characterization with computational analysis to reveal the intrinsic interfacial reaction mechanisms. Thirty-six challenges and problems being tackled in materials science ranging from topological quantum materials to data science/machine learning to electrochemical additive manufacturing to ab initio spin dynamics. Those who are a little more established can likely remember the youthful exuberance of starting out—labs to equip, students to recruit and train, proposals to write, manuscripts to draft and submit. Societal problems to solve. Science to discover. In the years to come, some of the listed challenges may be solved, some may stagnate, some may lead to even more challenges, like the head of a hydra (chop one off, two grow back). Like the self-selecting list of talented individuals presented here, the next generation of scientific problems (and opportunities) will grow in a self-sustaining manner, and we can only take a snapshot once in a while.}, number={8}, journal={MATTER}, author={Allen, Monica and Bediako, Kwabena and Bowman, William J. and Calabrese, Michelle and Caretta, Lucas and Cersonsky, Rose K. and Chen, Wen and Correa, Santiago and Davidson, Rachel and Dresselhaus-Marais, Leora and et al.}, year={2023}, month={Aug}, pages={2480–2487} }
 @article{a materials acceleration platform for organic laser discovery_2022, url={https://publons.com/wos-op/publon/57270242/}, DOI={10.26434/CHEMRXIV-2022-9ZM65}, abstractNote={Conventional materials discovery is a laborious and time-consuming process that can take decades from initial conception of the material to commercialization. Recent developments in materials acceleration platforms promise to accelerate materials discovery using automation of experiments coupled with machine learning. However, most of the automation efforts in chemistry focus on synthesis and compound identification, with integrated target property characterization receiving less attention. In this work, we introduce an automated platform for the discovery of molecules as gain mediums for organic semiconductor lasers, a problem that has been challenging for conventional approaches. Our platform encompassed automated lego-like synthesis, product identification, and optical characterization that can be executed in a fully integrated end-to-end fashion. Using this workflow to screen organic laser candidates, we have discovered 8 potential candidates for organic lasers. We tested the lasing threshold of 4 molecules in thin-film devices and found 2 molecules with state-of-the-art performance. These promising results show the potential of automated synthesis and screening for accelerated materials development.}, journal={ChemRxiv}, year={2022} }
 @article{augmenting polymer datasets by iterative rearrangement_2022, url={https://publons.com/wos-op/publon/57270214/}, DOI={10.26434/CHEMRXIV-2022-HXVCC}, abstractNote={One of the biggest obstacles to successful polymer property prediction is an effective representation that accurately captures the sequence of repeat units in a polymer. Motivated by the successes of data augmentation in computer vision and natural language processing, we explore augmenting polymer data by rearranging the molecular representation while preserving the correct connectivity, revealing additional substructural information that is not present in a single representation. We evaluate the effects of this technique on the performance of machine learning models trained on three experimental polymer datasets and compare them to common molecular representations. Data augmentation improves deep learning property prediction performance compared to equivalent (non-augmented) representations. In datasets where the target property is primarily influenced by the polymer sequence rather than experimental parameters, this data augmentation technique provides the molecular embedding with more information to improve property prediction accuracy.}, journal={ChemRxiv}, year={2022} }
 @article{seifrid_pollice_aguilar-granda_chan_hotta_ser_vestfrid_wu_aspuru-guzik_2022, title={Autonomous Chemical Experiments: Challenges and Perspectives on Establishing a Self-Driving Lab}, volume={8}, url={https://doi.org/10.1021/acs.accounts.2c00220}, DOI={10.1021/acs.accounts.2c00220}, abstractNote={Conspectus We must accelerate the pace at which we make technological advancements to address climate change and disease risks worldwide. This swifter pace of discovery requires faster research and development cycles enabled by better integration between hypothesis generation, design, experimentation, and data analysis. Typical research cycles take months to years. However, data-driven automated laboratories, or self-driving laboratories, can significantly accelerate molecular and materials discovery. Recently, substantial advancements have been made in the areas of machine learning and optimization algorithms that have allowed researchers to extract valuable knowledge from multidimensional data sets. Machine learning models can be trained on large data sets from the literature or databases, but their performance can often be hampered by a lack of negative results or metadata. In contrast, data generated by self-driving laboratories can be information-rich, containing precise details of the experimental conditions and metadata. Consequently, much larger amounts of high-quality data are gathered in self-driving laboratories. When placed in open repositories, this data can be used by the research community to reproduce experiments, for more in-depth analysis, or as the basis for further investigation. Accordingly, high-quality open data sets will increase the accessibility and reproducibility of science, which is sorely needed. In this Account, we describe our efforts to build a self-driving lab for the development of a new class of materials: organic semiconductor lasers (OSLs). Since they have only recently been demonstrated, little is known about the molecular and material design rules for thin-film, electrically-pumped OSL devices as compared to other technologies such as organic light-emitting diodes or organic photovoltaics. To realize high-performing OSL materials, we are developing a flexible system for automated synthesis via iterative Suzuki–Miyaura cross-coupling reactions. This automated synthesis platform is directly coupled to the analysis and purification capabilities. Subsequently, the molecules of interest can be transferred to an optical characterization setup. We are currently limited to optical measurements of the OSL molecules in solution. However, material properties are ultimately most important in the solid state (e.g., as a thin-film device). To that end and for a different scientific goal, we are developing a self-driving lab for inorganic thin-film materials focused on the oxygen evolution reaction. While the future of self-driving laboratories is very promising, numerous challenges still need to be overcome. These challenges can be split into cognition and motor function. Generally, the cognitive challenges are related to optimization with constraints or unexpected outcomes for which general algorithmic solutions have yet to be developed. A more practical challenge that could be resolved in the near future is that of software control and integration because few instrument manufacturers design their products with self-driving laboratories in mind. Challenges in motor function are largely related to handling heterogeneous systems, such as dispensing solids or performing extractions. As a result, it is critical to understand that adapting experimental procedures that were designed for human experimenters is not as simple as transferring those same actions to an automated system, and there may be more efficient ways to achieve the same goal in an automated fashion. Accordingly, for self-driving laboratories, we need to carefully rethink the translation of manual experimental protocols.}, journal={Accounts of Chemical Research}, publisher={American Chemical Society (ACS)}, author={Seifrid, Martin and Pollice, Robert and Aguilar-Granda, Andrés and Chan, Zamyla Morgan and Hotta, Kazuhiro and Ser, Cher Tian and Vestfrid, Jenya and Wu, Tony C. and Aspuru-Guzik, Alán}, year={2022}, month={Sep} }
 @article{autonomous chemical experiments: challenges and perspectives on establishing a self-driving lab_2022, url={https://publons.com/wos-op/publon/58870406/}, DOI={10.1021/ACS.ACCOUNTS.2C002202454}, journal={Accounts of Chemical Research}, year={2022} }
 @article{luginbuhl_ko_ran_hu_becwar_karki_seifrid_okubo_wang_ade_et al._2022, title={Low Voltage-Loss Organic Solar Cells Light the Way for Efficient Semitransparent Photovoltaics}, volume={3}, ISSN={["2367-198X"]}, url={https://doi.org/10.1002/solr.202200135}, DOI={10.1002/solr.202200135}, abstractNote={Organic solar cells that are transparent to visible light are highly desirable for applications such as window treatments or solar greenhouse panels. A key challenge is to simultaneously transmit most photons between 400 and 700 nm while retaining a high short‐circuit current and power conversion efficiency (PCE). Here, organic bulk heterojunction (BHJ) solar cells consisting of a donor polymer (PM2) is reported and the non‐fullerene acceptor ITIC‐Th achieves a PCE of 9.3%, and the BHJ thin films exhibit an average visible transmittance over 40%. This value is achieved primarily due to a very high open‐circuit voltage (VOC) of 0.93 V, which represents a voltage loss of only 0.50 V relative to the material optical bandgap, Eopt. In PM2:PC61BM devices, this voltage loss increases to 0.62 V (VOC = 0.82 V). It is found that this difference in VOC is due to higher nonradiative recombination in the fullerene‐based solar cell, suggesting that non‐fullerene acceptors may lead to better performance in semi‐transparent devices. The optoelectronic properties associated with PM2:ITIC‐Th and PM2:PC61BM blends are further corroborated by different morphological features and local structures at the donor‐acceptor interfaces characterized by atomic force microscopy, X‐ray scattering, and solid‐state NMR spectroscopy techniques.}, journal={SOLAR RRL}, publisher={Wiley}, author={Luginbuhl, Benjamin R. and Ko, Seo-Jin and Ran, Niva A. and Hu, Huawei and Becwar, Shona M. and Karki, Akchheta and Seifrid, Martin and Okubo, Takashi and Wang, Ming and Ade, Harald W. and et al.}, year={2022}, month={Mar} }
 @article{seifrid_hattrick-simpers_aspuru-guzik_kalil_cranford_2022, title={Reaching critical MASS: Crowdsourcing designs for the next generation of materials acceleration platforms}, volume={5}, url={http://dx.doi.org/10.1016/j.matt.2022.05.035}, DOI={10.1016/j.matt.2022.05.035}, abstractNote={Over the past decades, continual advancement of computational power has led to the prevalence of automation across science, industry, and society, whereby digital solutions were developed algorithmically to exploit technical knowledge of a problem. However, we are currently entering a critical period of computational innovation, particularly in materials science. Non-intuitive materials acceleration for societal solutions (MASS) can now potentially be uncovered by computational machine learning, artificial intelligence, and other self-driving methods for materials discovery. Herein, we invite the ideas for the next generation of materials acceleration platforms (MAPs). Help us reach critical mass of ideas and start a new materials reaction. Over the past decades, continual advancement of computational power has led to the prevalence of automation across science, industry, and society, whereby digital solutions were developed algorithmically to exploit technical knowledge of a problem. However, we are currently entering a critical period of computational innovation, particularly in materials science. Non-intuitive materials acceleration for societal solutions (MASS) can now potentially be uncovered by computational machine learning, artificial intelligence, and other self-driving methods for materials discovery. Herein, we invite the ideas for the next generation of materials acceleration platforms (MAPs). Help us reach critical mass of ideas and start a new materials reaction.}, number={7}, journal={Matter}, publisher={Elsevier BV}, author={Seifrid, Martin and Hattrick-Simpers, Jason and Aspuru-Guzik, Alán and Kalil, Tom and Cranford, Steve}, year={2022}, month={Jul}, pages={1972–1976} }
 @article{seifrid_hickman_aguilar-granda_lavigne_vestfrid_wu_gaudin_hopkins_aspuru-guzik_2022, title={Routescore: Punching the Ticket to More Efficient Materials Development}, volume={1}, url={https://doi.org/10.1021/acscentsci.1c01002}, DOI={10.1021/acscentsci.1c01002}, abstractNote={Self-driving laboratories, in the form of automated experimentation platforms guided by machine learning algorithms, have emerged as a potential solution to the need for accelerated science. While new tools for automated analysis and characterization are being developed at a steady rate, automated synthesis remains the bottleneck in the chemical space accessible to self-driving laboratories. Combining automated and manual synthesis efforts immediately significantly expands the explorable chemical space. To effectively direct the different capabilities of automated (higher throughput and less labor) and manual synthesis (greater chemical versatility), we describe a protocol, the RouteScore, that quantifies the cost of combined synthetic routes. In this work, the RouteScore is used to determine the most efficient synthetic route to a well-known pharmaceutical (structure-oriented optimization) and to simulate a self-driving laboratory that finds the most easily synthesizable organic laser molecule with specific photophysical properties from a space of ∼3500 possible molecules (property-oriented optimization). These two examples demonstrate the power and flexibility of our approach in mixed synthetic planning and optimization and especially in downselecting promising candidates from a large chemical space via an a priori estimation of the synthetic costs.}, journal={ACS Central Science}, publisher={American Chemical Society (ACS)}, author={Seifrid, Martin and Hickman, Riley J. and Aguilar-Granda, Andrés and Lavigne, Cyrille and Vestfrid, Jenya and Wu, Tony C. and Gaudin, Théophile and Hopkins, Emily J. and Aspuru-Guzik, Alán}, year={2022}, month={Jan} }
 @article{sizes of pure and doped helium droplets from single shot x-ray imaging_2022, url={https://publons.com/wos-op/publon/51650603/}, DOI={10.1063/5.0080342}, abstractNote={Advancements in x-ray free-electron lasers on producing ultrashort, ultrabright, and coherent x-ray pulses enable single-shot imaging of fragile nanostructures, such as superfluid helium droplets. This imaging technique gives unique access to the sizes and shapes of individual droplets. In the past, such droplet characteristics have only been indirectly inferred by ensemble averaging techniques. Here, we report on the size distributions of both pure and doped droplets collected from single-shot x-ray imaging and produced from the free-jet expansion of helium through a 5 μm diameter nozzle at 20 bars and nozzle temperatures ranging from 4.2 to 9 K. This work extends the measurement of large helium nanodroplets containing 109–1011 atoms, which are shown to follow an exponential size distribution. Additionally, we demonstrate that the size distributions of the doped droplets follow those of the pure droplets at the same stagnation condition but with smaller average sizes.}, journal={The Journal of Chemical Physics}, year={2022} }
 @article{halaby_martynowycz_zhu_tretiak_zhugayevych_gonen_seifrid_2021, title={Microcrystal Electron Diffraction for Molecular Design of Functional Non-Fullerene Acceptor Structures}, volume={33}, url={https://doi.org/10.1021/acs.chemmater.0c04111}, DOI={10.1021/acs.chemmater.0c04111}, abstractNote={Understanding the relationship between molecular structure and solid-state arrangement informs about the design of new organic semiconductor (OSC) materials with improved optoelectronic properties. However, determining their atomic structure remains challenging. Here, we report the lattice organization of two non-fullerene acceptors (NFAs) determined using microcrystal electron diffraction (MicroED) from crystals not traceable by X-ray crystallography. The MicroED structure of o-IDTBR was determined from a powder without crystallization, and a new polymorph of ITIC-Th is identified with the most distorted backbone of any NFA. Electronic structure calculations elucidate the relationships between molecular structures, lattice arrangements, and charge-transport properties for a number of NFA lattices. The high dimensionality of the connectivity of the 3D wire mesh topology is the best for robust charge transport within NFA crystals. However, some examples suffer from uneven electronic coupling. MicroED combined with advanced electronic structure modeling is a powerful new approach for structure determination, exploring polymorphism and guiding the design of new OSCs and NFAs.}, number={3}, journal={Chemistry of Materials}, publisher={American Chemical Society (ACS)}, author={Halaby, Steve and Martynowycz, Michael W. and Zhu, Ziyue and Tretiak, Sergei and Zhugayevych, Andriy and Gonen, Tamir and Seifrid, Martin}, year={2021}, month={Feb}, pages={966–977} }
 @article{microcrystal electron diffraction for molecular design of functional non-fullerene acceptor structures_2021, url={https://publons.com/wos-op/publon/55951474/}, DOI={10.1107/S0108767321097415}, abstractNote={A consequence of the small crystals used for microcrystal electron diffraction (MicroED) is that their diffraction patterns are composed of weak diffraction spots. In an effort to reduce radiation damage, the exposure is often reduced, which further exacerbates this effect. Efficient electron detectors are required to accurately recover the information in the diffraction spots, particularly at high resolution. Sensitive detectors, such as the direct electron detectors commonly used for imaging in electron cryo-microscopy (cryo-EM), can help measure the intensities of these weak reflections, but may introduce limitations at low resolution, where reflections tend to be much stronger. This presentation outlines our experiences with various electron detectors and explores the relationship between data collection, data processing, and camera performance. Recent examples of MicroED data from standard proteins will be shown and their improved data quality will be discussed.}, journal={Acta Crystallographica Section A: Foundations and Advances}, year={2021} }
 @article{seifrid_hickman_aguilar-granda_lavigne_vestfrid_wu_gaudin_hopkins_aspuru-guzik_2021, title={Routescore: Punching the Ticket to More Efficient Materials Development}, volume={7}, url={https://doi.org/10.26434/chemrxiv-2021-k0qx5}, DOI={10.26434/chemrxiv-2021-k0qx5}, abstractNote={Self-driving labs, in the form of automated experimentation platforms guided by machine learning algorithms have emerged as a potential solution to the need for accelerated science. While new tools for automated analysis and characterization are being developed at a steady rate, automated synthesis remains the bottleneck in the chemical space accessible to self-driving labs. Combining automated and manual synthesis efforts immediately significantly expands the explorable chemical space. To effectively direct the different capabilities of automated (higher throughput and less labor) and manual synthesis (greater chemical versatility), we describe a protocol, the RouteScore, that quantifies the cost of combined synthetic routes. In this work, the RouteScore is used to determine the most efficient synthetic route to a well-known pharmaceutical (structure-oriented optimization), and to simulate a self-driving lab that finds the most easily synthesizable organic laser molecule with specific photophysical properties from a space of ~3500 possible molecules (property-oriented optimization). These two examples demonstrate the power and generality of our approach in mixed synthetic planning and optimization.}, journal={ChemRxiv}, publisher={American Chemical Society (ACS)}, author={Seifrid, Martin and Hickman, Riley J. and Aguilar-Granda, Andrés and Lavigne, Cyrille and Vestfrid, Jenya and Wu, Tony C. and Gaudin, Théophile and Hopkins, Emily J. and Aspuru-Guzik, Alán}, year={2021}, month={Jul} }
 @article{seifrid_hickman_aguilar-granda_lavigne_vestfrid_wu_gaudin_hopkins_aspuru-guzik_2021, title={Routescore: Punching the Ticket to More Efficient Materials Development}, volume={7}, url={https://doi.org/10.33774/chemrxiv-2021-k0qx5}, DOI={10.33774/chemrxiv-2021-k0qx5}, abstractNote={Self-driving labs, in the form of automated experimentation platforms guided by machine learning algorithms have emerged as a potential solution to the need for accelerated science. While new tools for automated analysis and characterization are being developed at a steady rate, automated synthesis remains the bottleneck in the chemical space accessible to self-driving labs. Combining automated and manual synthesis efforts immediately significantly expands the explorable chemical space. To effectively direct the different capabilities of automated (higher throughput and less labor) and manual synthesis (greater chemical versatility), we describe a protocol, the RouteScore, that quantifies the cost of combined synthetic routes. In this work, the RouteScore is used to determine the most efficient synthetic route to a well-known pharmaceutical (structure-oriented optimization), and to simulate a self-driving lab that finds the most easily synthesizable organic laser molecule with specific photophysical properties from a space of ~3500 possible molecules (property-oriented optimization). These two examples demonstrate the power and generality of our approach in mixed synthetic planning and optimization.}, publisher={Cambridge University Press (CUP)}, author={Seifrid, Martin and Hickman, Riley J. and Aguilar-Granda, Andrés and Lavigne, Cyrille and Vestfrid, Jenya and Wu, Tony C. and Gaudin, Théophile and Hopkins, Emily J. and Aspuru-Guzik, Alán}, year={2021}, month={Jul} }
 @article{seifrid_aspuru-guzik_2021, title={You Wouldn’t Download a Molecule! Now, ChemSCAD Makes It Possible}, volume={7}, url={https://doi.org/10.1021/acscentsci.1c00108}, DOI={10.1021/acscentsci.1c00108}, abstractNote={Recently, three-dimensional (3D) printing has revolutionized manufacturing and made rapid prototyping possible. With this new manufacturing technique, it is now possible to design and create complex, intricate objects that are impossible to produce through any other means. The rise of 3D printing and the dwindling cost of the required hardware have been a boon to hobbyists and “makers” who can create objects in a matter of hours using cheap starting materials. These opportunities have also attracted the attention of many chemists due to the possibility of creating labware that may be cheaper or offers better performance than commercially available products as well as custom labware that simply isn’t commercially available. Notable examples range from simple microscopes to complex microfluidic systems, and even data visualization aids. In this issue of ACS Central Science, Cronin and co-workers report software that enables chemists to 3D print bespoke “reactionware” as part of their effort to make digitizing chemistry more accessible. By using labware tailored to a specific synthetic route, referred to as “reactionware”, the labor required for multistep syntheses can be substantially reduced. The authors have previously demonstrated the use of the reactionware concept to synthesize a variety of compounds. Three-dimensional printing makes this possible since the cost compared to, for example, custom glassware is so much lower. However, one of the chief challenges in creating custom 3D-printed parts is that they must be designed using computer-aided design (CAD) software, and most chemists lack the knowledge to use it. The Chemical Synthesis by Computer Aided Design (ChemSCAD) software has emerged from the Cronin group as a possible solution to this dilemma.}, number={2}, journal={ACS Central Science}, publisher={American Chemical Society (ACS)}, author={Seifrid, Martin and Aspuru-Guzik, Alán}, year={2021}, month={Feb}, pages={228–230} }
 @article{vollbrecht_lee_ko_brus_karki_le_seifrid_ford_cho_bazan_et al._2020, title={Design of narrow bandgap non-fullerene acceptors for photovoltaic applications and investigation of non-geminate recombination dynamics}, url={https://doi.org/10.1039/D0TC02136A}, DOI={10.1039/D0TC02136A}, abstractNote={A new narrow bandgap non-fullerene electron acceptor was designed, synthesized, and characterized for near-infrared organic photovoltaics.}, journal={Journal of Materials Chemistry C}, publisher={Royal Society of Chemistry (RSC)}, author={Vollbrecht, Joachim and Lee, Jaewon and Ko, Seo-Jin and Brus, Viktor V. and Karki, Akchheta and Le, William and Seifrid, Martin and Ford, Michael J. and Cho, Kilwon and Bazan, Guillermo C. and et al.}, year={2020} }
 @article{seifrid_reddy_chmelka_bazan_2020, title={Insight into the structures and dynamics of organic semiconductors through solid-state NMR spectroscopy}, url={https://doi.org/10.1038/s41578-020-00232-5}, DOI={10.1038/s41578-020-00232-5}, abstractNote={Organic semiconductors (OSCs) are of fundamental and technological interest, owing to their properties and functions in a range of optoelectronic devices, including organic light-emitting diodes, organic photovoltaics and organic field-effect transistors, as well as emerging technologies, such as bioelectronic devices. The solid-state organization of the subunits in OSC materials, whether molecular or polymeric, determines the properties relevant to device performance. Nevertheless, the systematic relationships between composition, structure and processing conditions are rarely fully understood, owing to the complexity of the organic architectures and the resulting solid-state structures. Characterization over different length scales and timescales is essential, especially for semi-ordered or amorphous regions, for which solid-state NMR (ssNMR) spectroscopy yields nanoscale insight that can be correlated with scattering measurements and macroscopic property analyses. In this Review, we assess recent results, challenges and opportunities in the application of ssNMR to OSCs, highlighting its role in state-of-the-art materials design and characterization. We illustrate how insight is obtained on local order and composition, interfacial structures, dynamics, interactions and how this information can be used to establish structure–property relationships. Finally, we provide our perspective on applying ssNMR to the next generation of OSCs and the development of new ssNMR methods. The structure of organic semiconductor thin films influences their performance in optoelectronic devices. This Review highlights how solid-state NMR techniques can be used to investigate the structure, composition and dynamics of organic semiconductors and, thus, establish structure–property relationships.}, journal={Nature Reviews Materials}, author={Seifrid, Martin and Reddy, G. N. Manjunatha and Chmelka, Bradley F. and Bazan, Guillermo C.}, year={2020}, month={Sep} }
 @article{sharma_lee_seifrid_gupta_bazan_yoo_2020, title={Performance enhancement of conjugated polymer-small molecule-non fullerene ternary organic solar cells by tuning recombination kinetics and molecular ordering}, volume={201}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85081658795&partnerID=MN8TOARS}, DOI={10.1016/j.solener.2020.03.008}, abstractNote={We present our study of conjugated polymer-small molecule (SM)-non-fullerene ternary organic solar cells (OSCs), which employs conjugated polymer PTB7-Th and small molecule p-DTS(FBTTh2)2 as donors and non-fullerene molecule IEICO-4F as an acceptor. It is observed that the power conversion efficiency (PCE) of ∼10.9% for PTB7-Th: p-DTS(FBTTh2)2: IEICO-4F ternary OSCs with 15 wt% of p-DTS(FBTTh2)2 SM is higher than PCE of ∼9.8% for PTB7-Th: IEICO-4F OSCs. Morphological studies confirm that the addition of p-DTS(FBTTh2)2 SM in PTB7-Th: IEICO-4F binary blend improves molecular ordering and crystallinity of PTB7-Th due to the favorable interaction with p-DTS(FBTTh2)2 thereby providing 3-D textured structures consisting of a mixture of edge-on and face-on orientations. The improved molecular ordering is shown to enhance exciton generation rate, exciton dissociation, charge collection, and to reduce charge recombination, all of which boosts the PCE.}, journal={Solar Energy}, author={Sharma, R. and Lee, H. and Seifrid, M. and Gupta, V. and Bazan, G.C. and Yoo, S.}, year={2020}, pages={499–507} }
 @article{robust unipolar electron conduction using an ambipolar polymer semiconductor with solution-processable blends_2020, url={http://dx.doi.org/10.1021/acs.chemmater.0c00234}, DOI={10.1021/acs.chemmater.0c00234}, abstractNote={Ambipolar polymer semiconductors are among the most ubiquitous semiconductors used for high mobility organic field-effect transistors. However, since ambipolar polymer semiconductors are capable of...}, journal={Chemistry of Materials}, year={2020}, month={Aug} }
 @article{xu_yuan_zhou_seifrid_ying_li_huang_bazan_ma_2019, title={Ambient Processable and Stable All-Polymer Organic Solar Cells}, volume={29}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85059856485&partnerID=MN8TOARS}, DOI={10.1002/adfm.201806747}, abstractNote={Abstract}, number={8}, journal={Advanced Functional Materials}, author={Xu, Y. and Yuan, J. and Zhou, S. and Seifrid, M. and Ying, L. and Li, B. and Huang, F. and Bazan, G.C. and Ma, W.}, year={2019} }
 @article{yurash_leifert_reddy_cao_biberger_brus_seifrid_santiago_köhler_chmelka_et al._2019, title={Atomic-Level Insight into the Postsynthesis Band Gap Engineering of a Lewis Base Polymer Using Lewis Acid Tris(pentafluorophenyl)borane}, volume={31}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85066411581&partnerID=MN8TOARS}, DOI={10.1021/acs.chemmater.9b01224}, abstractNote={In this report, we investigate the binding properties of the Lewis acid tris(pentafluorophenyl)borane with a Lewis base semiconducting polymer, PFPT, and the subsequent mechanism of band gap reduction. Experiments and quantum chemical calculations confirm that the formation of a Lewis acid adduct is energetically favorable (ΔG° < −0.2 eV), with preferential binding at the pyridyl nitrogen in the polymer backbone over other Lewis base sites. Upon adduct formation, ultraviolet photoelectron spectroscopy indicates only a slight decrease in the HOMO energy, implying that a larger reduction in the LUMO energy is primarily responsible for the observed optical band gap narrowing (ΔEopt = 0.3 eV). Herein, we also provide the first spatially resolved picture of how Lewis acid adducts form in heterogeneous, disordered polymer/tris(pentafluorophenyl)borane thin films via one- (1D) and two-dimensional (2D) solid-state nuclear magnetic resonance. Notably, solid-state 1D 11B, 13C{1H}, and 13C{19F} cross-polarization ma...}, number={17}, journal={Chemistry of Materials}, author={Yurash, B. and Leifert, D. and Reddy, G.N.M. and Cao, D.X. and Biberger, S. and Brus, V.V. and Seifrid, M. and Santiago, P.J. and Köhler, A. and Chmelka, B.F. and et al.}, year={2019}, pages={6715–6725} }
 @article{seifrid_reddy_zhou_chmelka_bazan_2019, title={Direct Observation of the Relationship betweenMolecular Topology and Bulk Morphology for a Ï€-Conjugated Material}, volume={141}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85063385330&partnerID=MN8TOARS}, DOI={10.1021/jacs.8b13200}, abstractNote={High-performance organic semiconducting materials are reliant upon subtle changes in structure across different length scales. These morphological features control relevant physical properties and ultimately device performance. By combining in situ NMR spectroscopy and theoretical calculations, the conjugated small molecule TT is shown to exhibit distinct temperature-dependent local structural features that are related to macroscopic properties. Specifically, lamellar and melt states are shown to exhibit different molecular topologies associated with planar and twisted conformations of TT, respectively. This topological transformation offers a novel avenue for molecular design and control of solid-state organization.}, number={13}, journal={Journal of the American Chemical Society}, author={Seifrid, M.T. and Reddy, G.N.M. and Zhou, C. and Chmelka, B.F. and Bazan, G.C.}, year={2019}, pages={5078–5082} }
 @article{lill_eftaiha_huang_yang_seifrid_wang_bazan_nguyen_2019, title={High-k Fluoropolymer Gate Dielectric in Electrically Stable Organic Field-Effect Transistors}, volume={11}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85065073199&partnerID=MN8TOARS}, DOI={10.1021/acsami.8b20827}, abstractNote={A detailed study of a high-k fluoropolymer gate dielectric material, poly(vinylidene fluoride- co-hexafluoropropylene) [P(VDF-HFP)], is presented as a guide to achieve low operational voltage and electrically stable device performance. The large dipole moment of C-F dipoles in P(VDF-HFP) is responsible for its high dielectric constant as well as its potentially ferroelectric behavior that must be minimized to avoid hysteretic current-voltage characteristics. A range of material grades and processing conditions are explored and are shown to have a significant effect on the degree of hysteresis observed in device-transfer characteristics. The percentage of HFP monomer in the P(VDF-HFP) dielectric has an effect on gate-dependent mobility induced by disorder at the semiconductor-dielectric interface. Most importantly, we present the considerations that must be made to achieve optimal performance in multiple device architectures of organic field-effect transistors when using P(VDF-HFP) as a dielectric layer.}, number={17}, journal={ACS Applied Materials and Interfaces}, author={Lill, A.T. and Eftaiha, A.F. and Huang, J. and Yang, H. and Seifrid, M. and Wang, M. and Bazan, G.C. and Nguyen, T.-Q.}, year={2019}, pages={15821–15828} }
 @article{lee_ko_lee_huang_zhu_seifrid_vollbrecht_brus_karki_wang_et al._2019, title={Side-Chain Engineering of Nonfullerene Acceptors for Near-Infrared Organic Photodetectors and Photovoltaics}, volume={4}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85066507756&partnerID=MN8TOARS}, DOI={10.1021/acsenergylett.9b00721}, abstractNote={Narrow bandgap n-type molecular semiconductors are relevant as key materials components for the fabrication near-infrared organic solar cells (OSCs) and organic photodetectors (OPDs). We thus desig...}, number={6}, journal={ACS Energy Letters}, author={Lee, J. and Ko, S.-J. and Lee, H. and Huang, J. and Zhu, Z. and Seifrid, M. and Vollbrecht, J. and Brus, V.V. and Karki, A. and Wang, H. and et al.}, year={2019}, pages={1401–1409} }
 @article{side-chain engineering of nonfullerene acceptors for near-infrared organic photodetectors and photovoltaics (vol 4, pg 1401, 2019)_2019, url={https://publons.com/wos-op/publon/21044512/}, DOI={10.1021/ACSENERGYLETT.9B01263}, abstractNote={ADVERTISEMENT RETURN TO ISSUEPREVAddition/CorrectionNEXTORIGINAL ARTICLEThis notice is a correctionCorrection to "Side-Chain Engineering of Nonfullerene Acceptors for Near-Infrared Organic Photodetectors and Photovoltaics"Jaewon LeeJaewon LeeMore by Jaewon Lee, Seo-Jin KoSeo-Jin KoMore by Seo-Jin Ko, Hansol LeeHansol LeeMore by Hansol Lee, Jianfei HuangJianfei HuangMore by Jianfei Huanghttp://orcid.org/0000-0002-4051-4482, Ziyue ZhuZiyue ZhuMore by Ziyue Zhu, Martin SeifridMartin SeifridMore by Martin Seifrid, Joachim VollbrechtJoachim VollbrechtMore by Joachim Vollbrechthttp://orcid.org/0000-0002-0001-6913, Viktor V. BrusViktor V. BrusMore by Viktor V. Brushttp://orcid.org/0000-0002-8839-124X, Akchheta KarkiAkchheta KarkiMore by Akchheta Karki, Benjamin R. LuginbuhlBenjamin R. LuginbuhlMore by Benjamin R. Luginbuhl, Hengbin WangHengbin WangMore by Hengbin Wang, Kilwon Cho*Kilwon ChoMore by Kilwon Chohttp://orcid.org/0000-0003-0321-3629, Thuc-Quyen Nguyen*Thuc-Quyen NguyenMore by Thuc-Quyen Nguyenhttp://orcid.org/0000-0002-8364-7517, and Guillermo C. Bazan*Guillermo C. BazanMore by Guillermo C. Bazanhttp://orcid.org/0000-0002-2537-0310Cite this: ACS Energy Lett. 2019, 4, 7, 1732Publication Date (Web):June 27, 2019Publication History Published online27 June 2019Published inissue 12 July 2019https://pubs.acs.org/doi/10.1021/acsenergylett.9b01263https://doi.org/10.1021/acsenergylett.9b01263correctionACS PublicationsCopyright © 2019 American Chemical Society. This publication is available under these Terms of Use. Request reuse permissions This publication is free to access through this site. Learn MoreArticle Views1850Altmetric-Citations2LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail PDF (289 KB) Get e-Alertsclose Get e-Alerts}, journal={ACS Energy Letters}, year={2019} }
 @article{yurash_cao_brus_leifert_wang_dixon_seifrid_mansour_lungwitz_liu_et al._2019, title={Towards understanding the doping mechanism of organic semiconductors by Lewis acids}, volume={18}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85073832991&partnerID=MN8TOARS}, DOI={10.1038/s41563-019-0479-0}, abstractNote={Precise doping of organic semiconductors allows control over the conductivity of these materials, an essential parameter in electronic applications. Although Lewis acids have recently shown promise as dopants for solution-processed polymers, their doping mechanism is not yet fully understood. In this study, we found that B(C6F5)3 is a superior dopant to the other Lewis acids investigated (BF3, BBr3 and AlCl3). Experiments indicate that Lewis acid-base adduct formation with polymers inhibits the doping process. Electron-nuclear double-resonance and nuclear magnetic resonance experiments, together with density functional theory, show that p-type doping occurs by generation of a water-Lewis acid complex with substantial Brønsted acidity, followed by protonation of the polymer backbone and electron transfer from a neutral chain segment to a positively charged, protonated one. This study provides insight into a potential path for protonic acid doping and shows how trace levels of water can transform Lewis acids into powerful Brønsted acids.}, number={12}, journal={Nature Materials}, author={Yurash, B. and Cao, D.X. and Brus, V.V. and Leifert, D. and Wang, M. and Dixon, A. and Seifrid, M. and Mansour, A.E. and Lungwitz, D. and Liu, T. and et al.}, year={2019}, pages={1327–1334} }
 @article{karki_wetzelaer_reddy_náda?dy_seifrid_schauer_bazan_chmelka_blom_nguyen_2019, title={Unifying Energetic Disorder from Charge Transport and Band Bending in Organic Semiconductors}, volume={29}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85063411891&partnerID=MN8TOARS}, DOI={10.1002/adfm.201901109}, abstractNote={Abstract}, number={20}, journal={Advanced Functional Materials}, author={Karki, A. and Wetzelaer, G.-J.A.H. and Reddy, G.N.M. and Náda?dy, V. and Seifrid, M. and Schauer, F. and Bazan, G.C. and Chmelka, B.F. and Blom, P.W.M. and Nguyen, T.-Q.}, year={2019} }
 @article{lee_ko_seifrid_lee_luginbuhl_karki_ford_rosenthal_cho_nguyen_et al._2018, title={Bandgap Narrowing in Non-Fullerene Acceptors: Single Atom Substitution Leads to High Optoelectronic Response Beyond 1000 nm}, volume={8}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85051735533&partnerID=MN8TOARS}, DOI={10.1002/aenm.201801212}, abstractNote={Abstract}, number={24}, journal={Advanced Energy Materials}, author={Lee, J. and Ko, S.-J. and Seifrid, M. and Lee, H. and Luginbuhl, B.R. and Karki, A. and Ford, M. and Rosenthal, K. and Cho, K. and Nguyen, T.-Q. and et al.}, year={2018} }
 @article{lee_ko_seifrid_lee_mcdowell_luginbuhl_karki_cho_nguyen_bazan_2018, title={Design of Nonfullerene Acceptors with Near-Infrared Light Absorption Capabilities}, volume={8}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85050808600&partnerID=MN8TOARS}, DOI={10.1002/aenm.201801209}, abstractNote={Abstract}, number={26}, journal={Advanced Energy Materials}, author={Lee, J. and Ko, S.-J. and Seifrid, M. and Lee, H. and McDowell, C. and Luginbuhl, B.R. and Karki, A. and Cho, K. and Nguyen, T.-Q. and Bazan, G.C.}, year={2018} }
 @article{hughes_rosenthal_ran_seifrid_bazan_nguyen_2018, title={Determining the Dielectric Constants of Organic Photovoltaic Materials Using Impedance Spectroscopy}, volume={28}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85051139556&partnerID=MN8TOARS}, DOI={10.1002/adfm.201801542}, abstractNote={Abstract}, number={32}, journal={Advanced Functional Materials}, author={Hughes, M.P. and Rosenthal, K.D. and Ran, N.A. and Seifrid, M. and Bazan, G.C. and Nguyen, T.-Q.}, year={2018} }
 @article{mcdowell_narayanaswamy_yadagiri_gayathri_seifrid_datt_ryno_heifner_gupta_risko_et al._2018, title={Impact of rotamer diversity on the self-assembly of nearly isostructural molecular semiconductors}, volume={6}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85040173909&partnerID=MN8TOARS}, DOI={10.1039/c7ta09972j}, abstractNote={Switching bithiophene for thienothiophene reduces the number of rotational conformations, facilitating self-assembly with minimal effects on the electronic structure.}, number={2}, journal={Journal of Materials Chemistry A}, author={McDowell, C. and Narayanaswamy, K. and Yadagiri, B. and Gayathri, T. and Seifrid, M. and Datt, R. and Ryno, S.M. and Heifner, M.C. and Gupta, V. and Risko, C. and et al.}, year={2018}, pages={383–394} }
 @article{seifrid_oosterhout_toney_bazan_2018, title={Kinetic Versus Thermodynamic Orientational Preferences for a Series of Isomorphic Molecular Semiconductors}, volume={3}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85052760491&partnerID=MN8TOARS}, DOI={10.1021/acsomega.8b01435}, abstractNote={Due to the anisotropic nature of charge transport through most organic semiconductors, the orientation of the conjugated backbone is of great relevance because it may affect final device properties. Herein, we present a set of four nearly isostructural molecular organic semiconducting materials whose orientation changes drastically with a two-atom change in the conjugated framework. We investigate the X-ray diffraction patterns of these materials in the thin film, both as-deposited from solution and following melt-annealing. Following melt-annealing of the films, crystallites of all four materials orient edge-on with respect to the substrate, which indicates that this orientation is thermodynamically preferred. We can infer that the initial face-on orientation of some of the materials is due to kinetic trapping during the spin-coating process. Previous observations from the literature suggest that the edge-on orientation is the thermodynamically preferable state for many organic semiconducting materials. However, a cohesive explanation for this phenomenon remains elusive.}, number={8}, journal={ACS Omega}, author={Seifrid, M.T. and Oosterhout, S.D. and Toney, M.F. and Bazan, G.C.}, year={2018}, pages={10198–10204} }
 @article{wang_feng_seifrid_wang_liu_bazan_2017, title={Antibacterial Narrow-Band-Gap Conjugated Oligoelectrolytes with High Photothermal Conversion Efficiency}, volume={56}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85037327037&partnerID=MN8TOARS}, DOI={10.1002/anie.201709887}, abstractNote={Abstract}, number={50}, journal={Angewandte Chemie - International Edition}, author={Wang, B. and Feng, G. and Seifrid, M. and Wang, M. and Liu, B. and Bazan, G.C.}, year={2017}, pages={16063–16066} }
 @article{seifrid_ford_li_koh_trefonas_bazan_2017, title={Electrical Performance of a Molecular Organic Semiconductor under Thermal Stress}, volume={29}, url={http://dx.doi.org/10.1002/adma.201605511}, DOI={10.1002/adma.201605511}, abstractNote={The high temperature performance oforganic field-effect transistorsbased on a molecular organic semiconductor with intermediate dimensions, namely X2, is evaluated. Hole mobility is stable, even at 200-250 °C. Changes in device characteristics at high temperature are reversible across multiple cycles of high temperature operation. Measurements at high temperature exhibit larger hysteresis, while at low temperature one observes the emergence of ambipolar transport.}, number={12}, journal={Advanced Materials}, author={Seifrid, M. and Ford, M.J. and Li, M. and Koh, K.M. and Trefonas, P. and Bazan, G.C.}, year={2017}, month={Mar} }
 @article{wang_ford_zhou_seifrid_nguyen_bazan_2017, title={Linear Conjugated Polymer Backbones Improve Alignment in Nanogroove-Assisted Organic Field-Effect Transistors}, volume={139}, url={http://dx.doi.org/10.1021/jacs.7b10332}, DOI={10.1021/jacs.7b10332}, abstractNote={Three cyclopentadithiophene-difluorophenylene copolymers (named PhF2,3, PhF2,5, and PhF2,6), which differ by the arrangement of fluorines on the phenylene structural unit, were designed and synthesized for the fabrication of organic field-effect transistors (OFETs). Single crystal structures of model compounds representative of the backbone and density functional theory (DFT) were used to estimate the backbone shape for each copolymer. The different substitution arrangements impact the backbone secondary structure through different nonbonding F···H interactions. PhF2,5 and PhF2,6 assumed more linear backbones relative to PhF2,3, which in turn impacts self-assembly and polymer chain alignment on nanogrooved (NG) substrates. A larger improvement of charge carrier mobility for the more linear backbones was achieved when using NG substrates. Among the three polymers, PhF2,6 achieved the highest average field-effect hole mobility (5.1 cm2 V-1 s-1). As evidenced by grazing incidence wide-angle X-ray scattering (GIWAXS), thin films of PF2,5 and PF2,6 exhibited a higher degree of anisotropic alignment, relative to the more curved PF2,3 counterpart, consistent with the higher hole mobilities. This work gives insight into how nonbonding interactions can influence charge carrier mobility through changes in secondary structure and suggests that polymers with more linear shapes might be preferred for achieving greater levels of alignment within the confines of a NG environment.}, number={48}, journal={Journal of the American Chemical Society}, author={Wang, M. and Ford, M.J. and Zhou, C. and Seifrid, M. and Nguyen, T.-Q. and Bazan, G.C.}, year={2017}, month={Dec}, pages={17624–17631} }
 @article{topological transformation of π-conjugated molecules reduces resistance to crystallization_2017, url={http://dx.doi.org/10.1002/ange.201702646}, DOI={10.1002/ange.201702646}, abstractNote={Abstract}, journal={Angewandte Chemie}, year={2017}, month={Aug} }
 @article{zhou_cui_mcdowell_seifrid_chen_brédas_wang_huang_bazan_2017, title={Topological Transformation of π-Conjugated Molecules Reduces Resistance to Crystallization}, volume={56}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85021823643&partnerID=MN8TOARS}, DOI={10.1002/anie.201702646}, abstractNote={Abstract}, number={32}, journal={Angewandte Chemie - International Edition}, author={Zhou, C. and Cui, Q. and McDowell, C. and Seifrid, M. and Chen, X. and Brédas, J.-L. and Wang, M. and Huang, F. and Bazan, G.C.}, year={2017}, pages={9318–9321} }
 @article{synthesis and characterization of phosphorescent complexes containing corannulene_2015, url={https://publons.com/wos-op/publon/58870387/}, journal={Abstracts of Papers of the American Chemical Society}, year={2015} }
 @article{facendola_seifrid_siegel_djurovich_thompson_2015, title={Synthesis and characterization of phosphorescent platinum and iridium complexes with cyclometalated corannulene}, volume={44}, url={http://dx.doi.org/10.1039/c4dt03541k}, DOI={10.1039/c4dt03541k}, abstractNote={Novel, emissive Pt(ii) and Ir(iii) complexes are the first to have measured rates of inversion of cyclometalated corannulene.}, number={18}, journal={Dalton Transactions}, author={Facendola, J.W. and Seifrid, M. and Siegel, J. and Djurovich, P.I. and Thompson, M.E.}, year={2015}, pages={8456–8466} }
 @article{gomez_ferguson_cryan_bacellar_tanyag_jones_schorb_anielski_belkacem_bernando_et al._2014, title={Shapes and vorticities of superfluid helium nanodroplets}, volume={345}, url={http://dx.doi.org/10.1126/science.1252395}, DOI={10.1126/science.1252395}, abstractNote={X-raying superfluid helium droplets}, number={6199}, journal={Science}, author={Gomez, L.F. and Ferguson, K.R. and Cryan, J.P. and Bacellar, C. and Tanyag, R.M.P. and Jones, C. and Schorb, S. and Anielski, D. and Belkacem, A. and Bernando, C. and et al.}, year={2014}, month={Aug}, pages={906–909} }