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Global discovery of stable and non-toxic hybrid organic-inorganic perovskites for photovoltaic systems by combining machine learning method with first principle calculations

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Screening for lead-free inorganic double perovskites with suitable band gaps and high stability using combined machine learning and DFT calculation

Gao, Z., Zhang, H., Mao, G., Ren, J., Chen, Z., Wu, C., Gates, I. D., Yang, W., Ding, X., & Yao, J. (2021) · Applied Surface Science · doi:10.1016/j.apsusc.2021.150916 · Cited by 101

Building data-driven models with microstructural images: Generalization and interpretability

Ling, J., Hutchinson, M., Antono, E., DeCost, B., Holm, E. A., & Meredig, B. (2017) · Materials Discovery · doi:10.1016/j.md.2018.03.002 · Cited by 98

Magnetic and superconducting phase diagrams and transition temperatures predicted using text mining and machine learning

Court, C. J., & Cole, J. M. (2020) · npj Computational Materials · doi:10.1038/s41524-020-0287-8 · Cited by 104

Machine Learning-Assisted Development of Organic Solar Cell Materials: Issues, Analyses, and Outlooks

Miyake, Y., & Saeki, A. (2021) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.1c03526 · Cited by 99

Benchmarking DFT Functionals for Excited-State Calculations of Donor–Acceptor TADF Emitters: Insights on the Key Parameters Determining Reverse Inter-System Crossing

Hall, D., Sancho-Garcı́a, J. C., Pershin, A., Beljonne, D., Zysman‐Colman, E., & Olivier, Y. (2023) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.2c08201 · Cited by 100

An artificial intelligence-aided virtual screening recipe for two-dimensional materials discovery

Sorkun, M. C., Astruc, S., Koelman, J. M. V. A., & Er, S. (2020) · npj Computational Materials · doi:10.1038/s41524-020-00375-7 · Cited by 94

Featurization strategies for polymer sequence or composition design by machine learning

Patel, R. A., Borca, C. H., & Webb, M. A. (2022) · Molecular Systems Design & Engineering · doi:10.1039/d1me00160d · Cited by 106

Computational Discovery of Organic LED Materials

Gómez‐Bombarelli, R., & Aspuru‐Guzik, A. (2018) · Royal Society of Chemistry eBooks · doi:10.1039/9781788010122-00423 · Cited by 4

Turbocharged molecular discovery of OLED emitters: from high-throughput quantum simulation to highly efficient TADF devices

Gómez‐Bombarelli, R., Aguilera‐Iparraguirre, J., Hirzel, T. D., Ha, D.-G., Einzinger, M., Wu, T., Baldo, M. A., & Aspuru‐Guzik, A. (2016) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2236966 · Cited by 5

Automatic Screen‐out of Ir(III) Complex Emitters by Combined Machine Learning and Computational Analysis

Cheng, Z., Liu, J., Jiang, T., Chen, M., Dai, F., Gao, Z., Ke, G., Zhao, Z., & Ou, Q. (2023) · Advanced Optical Materials · doi:10.1002/adom.202301093 · Cited by 26

High-throughput quantum chemistry and virtual screening for OLED material components

Halls, M. D., Giesen, D. J., Hughes, T. F., Goldberg, A., & Cao, Y. (2013) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2025092 · Cited by 12

Virtual Screening for Organic Solar Cells and Light Emitting Diodes

Forero‐Martinez, N. C., Lin, K., Kremer, K., & Andrienko, D. (2022) · Advanced Science · doi:10.1002/advs.202200825 · Cited by 23