@article{orouji_bennett_sadeghi_abolhasani_2024, title={Digital Pareto-front mapping of homogeneous catalytic reactions}, volume={3}, ISSN={["2058-9883"]}, url={https://doi.org/10.1039/D3RE00673E}, DOI={10.1039/d3re00673e}, abstractNote={We present a digital framework for rapid multi-objective reaction space exploration and optimization of homogeneous catalytic reactions through autonomous experimentation and Bayesian optimization.}, journal={REACTION CHEMISTRY & ENGINEERING}, author={Orouji, Negin and Bennett, Jeffrey A. and Sadeghi, Sina and Abolhasani, Milad}, year={2024}, month={Mar} }
 @article{bateni_sadeghi_orouji_bennett_punati_stark_wang_rosko_chen_castellano_et al._2024, title={Smart Dope: A Self-Driving Fluidic Lab for Accelerated Development of Doped Perovskite Quantum Dots (Adv. Energy Mater. 1/2024)}, volume={14}, ISSN={["1614-6840"]}, DOI={10.1002/aenm.202470001}, abstractNote={Self Driving Lab In article number 2302303,Milad Abolhasani and co-workers present a self-driving lab, called Smart Dope, for the fast-tracked discovery of doped quantum dots (QDs) for applications in clean energy technologies. Smart Dope utilizes machine learning-guided operation of flow reactors integrated with an in-situ characterizationmodule in a ‘closed-loop’ fashion to discover the best-in-class QD within one day of autonomous experiments.}, number={1}, journal={ADVANCED ENERGY MATERIALS}, author={Bateni, Fazel and Sadeghi, Sina and Orouji, Negin and Bennett, Jeffrey A. and Punati, Venkat S. and Stark, Christine and Wang, Junyu and Rosko, Michael C. and Chen, Ou and Castellano, Felix N. and et al.}, year={2024}, month={Jan} }
 @article{sadeghi_bateni_kim_son_bennett_orouji_punati_stark_cerra_awad_et al._2023, title={Autonomous nanomanufacturing of lead-free metal halide perovskite nanocrystals using a self-driving fluidic lab}, volume={12}, ISSN={["2040-3372"]}, DOI={10.1039/d3nr05034c}, abstractNote={We present a self-driving fluidic lab for accelerated synthesis science studies of lead-free metal halide perovskite nanocrystals.}, journal={NANOSCALE}, author={Sadeghi, Sina and Bateni, Fazel and Kim, Taekhoon and Son, Dae Yong and Bennett, Jeffrey A. and Orouji, Negin and Punati, Venkat S. and Stark, Christine and Cerra, Teagan D. and Awad, Rami and et al.}, year={2023}, month={Dec} }
 @article{morteza_yahyaeian_mirzaeibonehkhater_sadeghi_mohaimeni_taheri_2023, title={Deep learning hyperparameter optimization: Application to electricity and heat demand prediction for buildings}, volume={289}, ISSN={["1872-6178"]}, DOI={10.1016/j.enbuild.2023.113036}, abstractNote={Optimal planning and operation studies of modern energy systems are tied up with medium to long-term predictions of energy demand. Deep learning algorithms have recently become significantly useful in this regard because of their capacity to learn load patterns with a high degree of fluctuation and uncertainty. Yet, empirical evaluation of different configurations of deep learning models, for enhancing prediction accuracy, has been mostly ignored in the preceding literature. In this paper, different architectures of deep recurrent neural networks (DRNNs) are explored and adjusted specifically for the purpose of performing medium- and long-term predictions of energy demands. We intend to create a bespoke DRNN for heating and electricity consumption prediction with a 1-h resolution. Moreover, hyperparameter optimization, which is a time-consuming and rigorous task in deep learning algorithms due to their abundance, dependence on the particular application, and empirical nature, is studied comprehensively. In this respect, the space of possible configuration variables is thoroughly explored to identify a collection of hyperparameters that enables the DRNN model to more accurately forecast energy consumption. We also benchmark the proposed algorithm’s performance against other data-driven models, namely support vector machine (SVM) and gradient boosting (GB) regression. The proposed model outperforms SVM by 5.4% and GB regression by 7.0% in terms of energy forecasting accuracy.}, journal={ENERGY AND BUILDINGS}, author={Morteza, Azita and Yahyaeian, Amir Abbas and Mirzaeibonehkhater, Marzieh and Sadeghi, Sina and Mohaimeni, Ali and Taheri, Saman}, year={2023}, month={Jun} }
 @article{wang_sadeghi_velayati_paul_hetzler_danilov_ligler_wei_2023, title={Low-rate smartphone videoscopy for microsecond luminescence lifetime imaging with machine learning}, volume={2}, ISSN={["2752-6542"]}, url={https://doi.org/10.1093/pnasnexus/pgad313}, DOI={10.1093/pnasnexus/pgad313}, abstractNote={Abstract}, number={10}, journal={PNAS NEXUS}, author={Wang, Yan and Sadeghi, Sina and Velayati, Alireza and Paul, Rajesh and Hetzler, Zach and Danilov, Evgeny and Ligler, Frances S. and Wei, Qingshan}, editor={Reis, RuiEditor}, year={2023}, month={Sep} }
 @article{bateni_sadeghi_orouji_bennett_punati_stark_wang_rosko_chen_castellano_et al._2023, title={Smart Dope: A Self-Driving Fluidic Lab for Accelerated Development of Doped Perovskite Quantum Dots}, volume={11}, ISSN={["1614-6840"]}, DOI={10.1002/aenm.202302303}, abstractNote={Abstract}, journal={ADVANCED ENERGY MATERIALS}, author={Bateni, Fazel and Sadeghi, Sina and Orouji, Negin and Bennett, Jeffrey A. and Punati, Venkat S. and Stark, Christine and Wang, Junyu and Rosko, Michael C. and Chen, Ou and Castellano, Felix N. and et al.}, year={2023}, month={Nov} }