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RESEARCH PAPER · Communications Materials · 2022

Machine learned synthesizability predictions aided by density functional theory

Andrew S. Lee, Suchismita Sarker, James E. Saal, Logan T. Ward, Christopher K. H. Borg, Apurva Mehta, Christopher Wolverton

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42 direct citing works

Machine learned synthesizability predictions aided by density functional theoryOriginalKnowledge-integrated machine learning for materials: lessons from gameplaying and robotics137Machine‐Learning‐Assisted Determination of the Global Zero‐Temperature Phase Diagram of Materials109Accelerating the prediction of stable materials with machine learning104AI-enabled materials discovery for advanced ceramic electrochemical cells28Atomic Scale Design of MXenes and Their Parent Materials─From Theoretical and Experimental Perspectives180E_min: A First-Principles Thermochemical Descriptor for Predicting Molecular Synthesizability1Memory Effect on the Synthesis of Perovskite-Type Li-Ion Conductor LixLa2/3–x/3TiO3 (LLTO)15Emin: A First-Principles Thermochemical Descriptor for Predicting Molecular Synthesizability5Machine learning for efficient grazing-exit x-ray absorption near edge structure spectroscopy analysis: Bayesian optimization approach4Machine learning-enhanced optimal catalyst selection for water-gas shift reaction7Augmented chalcopyrites: A search for new Cu-In-Te phases0Dual Polyanion Mechanism for Superionic Transport in BH4‐Based Argyrodites5Multiscale computational modeling techniques in study and design of 2D materials: recent advances, challenges, and opportunities25Can Deep Learning Search for Exceptional Chiroptical Properties? The Halogenated [6]Helicene Case1Can Deep Learning Search for Exceptional Chiroptical Properties? The Halogenated [6]Helicene Case14Predicting and Accelerating Nanomaterial Synthesis Using Machine Learning Featurization31Crystal synthesizability prediction using contrastive positive unlabeled learning6SynCoTrain: a dual classifier PU-learning framework for synthesizability prediction9How to accelerate the inorganic materials synthesis: from computational guidelines to data-driven method?9Mechanistically Interpretable AI for Accelerated Energy Materials Design0Lasso Model-Based Optimization of CNC/CNF/rGO Nanocomposites5Drawing boundaries between feasible and unfeasible zeolite intergrowths using high-throughput computational screening with synthesis validation10Machine learning-accelerated screening of all-inorganic perovskites for tandem solar cells1Informatics for thermodynamics: Stability to synthesizability0Optical and polaronic properties of vacancy-ordered double perovskites: A first-principles investigation4Bridging theory and experiment in materials discovery: Machine-learning-assisted prediction of synthesizable structures3Do's and Don'ts When Visiting Circularly Polarized Luminescence9Beyond accuracy: comprehensive alignment for AI-driven molecular design1Mechanistically Interpretable Artificial Intelligence for Designing Oxygen Electrocatalysts0

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  1. Knowledge-integrated machine learning for materials: lessons from gameplaying and robotics

    Nature Reviews Materials · 2023-01-24

    Kedar Hippalgaonkar, Qianxiao Li, Xiaonan Wang, John W. Fisher, James Kirkpatrick, Tonio Buonassisi

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  2. Machine‐Learning‐Assisted Determination of the Global Zero‐Temperature Phase Diagram of Materials

    Advanced Materials · 2023-03-22

    Jonathan Schmidt, Noah Hoffmann, Hai‐Chen Wang, Pedro Borlido, Pedro J. M. A. Carriço, Tiago F. T. Cerqueira, Silvana Botti, Miguel A. L. Marques

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  3. Accelerating the prediction of stable materials with machine learning

    Nature Computational Science · 2023-11-09

    Sean D. Griesemer, Yi Xia, Chris M. Wolverton

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  4. AI-enabled materials discovery for advanced ceramic electrochemical cells

    Energy and AI · 2023-11-09

    Idris Temitope Bello, Ridwan Taiwo, Oladapo Christopher Esan, Adesola Habeeb Adegoke, Ahmed Olanrewaju Ijaola, Zheng Li, Siyuan Zhao, Chen Wang, Zongping Shao, Meng Ni

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  5. Atomic Scale Design of MXenes and Their Parent Materials─From Theoretical and Experimental Perspectives

    Chemical Reviews · 2023-11-17

    Jie Zhou, Martin Dahlqvist, Jonas Björk, Johanna Rosén

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  6. E_min: A First-Principles Thermochemical Descriptor for Predicting Molecular Synthesizability

    ChemRxiv · 2024-01-30

    Andrew Lee, Sarah N. Elliott, Hassan Harb, Logan T. Ward, Ian T Foster, Larry A. Curtiss, Rajeev S. Assary

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  7. Memory Effect on the Synthesis of Perovskite-Type Li-Ion Conductor LixLa2/3–x/3TiO3 (LLTO)

    Chemistry of Materials · 2024-01-31

    Matthew S. Chambers, Jiadong Chen, Robert Lee Sacci, Rebecca D. McAuliffe, Wenhao Q. Sun, Gabriel M. Veith

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  8. Emin: A First-Principles Thermochemical Descriptor for Predicting Molecular Synthesizability

    Journal of Chemical Information and Modeling · 2024-02-15

    Andrew S. Lee, Sarah N. Elliott, Hassan Harb, Logan T. Ward, Ian T Foster, Larry A. Curtiss, Rajeev S. Assary

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  9. Machine learning for efficient grazing-exit x-ray absorption near edge structure spectroscopy analysis: Bayesian optimization approach

    Machine Learning Science and Technology · 2024-04-23

    Cafer Tufan Cakir, Can Bogoclu, Franziska Emmerling, Christina Streli, Ana Guilherme Buzanich, Martin Radtke

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  10. Machine learning-enhanced optimal catalyst selection for water-gas shift reaction

    Digital Chemical Engineering · 2024-06-25

    Rahul Golder, S. Sarkar Pal, S. Che, Koustuv Ray

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  11. Augmented chalcopyrites: A search for new Cu-In-Te phases

    Physical Review Materials · 2024-08-26

    Andy Paul Chen, Wei Nong, Maung Thway, Jose Recatala‐Gomez, Haiwen Dai, Wenhao Zhai, D. V. Maheswar Repaka, Kedar Hippalgaonkar

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  12. Dual Polyanion Mechanism for Superionic Transport in BH4‐Based Argyrodites

    Advanced Energy Materials · 2024-09-03

    Pengbo Wang, Haoyu Liu, Sawankumar V. Patel, Jason E. Roberts, Yudan Chen, Bright O. Ogbolu, Brian E. Francisco, Yan‐Yan Hu

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  13. Multiscale computational modeling techniques in study and design of 2D materials: recent advances, challenges, and opportunities

    2D Materials · 2024-09-09

    Mohsen Asle Zaeem, Siby Thomas, Sepideh Kavousi, Ning Zhang, Tanmoy Mukhopadhyay, Avik Mahata

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  14. Can Deep Learning Search for Exceptional Chiroptical Properties? The Halogenated [6]Helicene Case

    Angewandte Chemie · 2024-09-27

    Rafael G. Uceda, Alfonso Gijón, Sandra Míguez‐Lago, Carlos M. Cruz, Víctor Blanco, Fátima Fernández-Álvarez, Luı́s Álvarez de Cienfuegos, Miguel Molina-Solana, Juan Gómez‐Romero, D. Miguel, Antonio J. Mota, Juan M. Cuerva

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  15. Can Deep Learning Search for Exceptional Chiroptical Properties? The Halogenated [6]Helicene Case

    Angewandte Chemie International Edition · 2024-09-27

    Rafael G. Uceda, Alfonso Gijón, Sandra Míguez‐Lago, Carlos M. Cruz, Víctor Blanco, Fátima Fernández-Álvarez, Luı́s Álvarez de Cienfuegos, Miguel Molina-Solana, Juan Gómez‐Romero, D. Miguel, Antonio J. Mota, Juan M. Cuerva

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  16. Predicting and Accelerating Nanomaterial Synthesis Using Machine Learning Featurization

    Nano Letters · 2024-11-12

    Christopher C. Price, Yansong Li, Guanyu Zhou, Rehan Younas, Spencer S. Zeng, Tim H. Scanlon, Jason M. Munro, Christopher L. Hinkle

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  17. Crystal synthesizability prediction using contrastive positive unlabeled learning

    Computer Physics Communications · 2024-12-07

    Tao Sun, Jianmei Yuan

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  18. SynCoTrain: a dual classifier PU-learning framework for synthesizability prediction

    Digital Discovery · 2025-01-01

    Sasan Amariamir, Janine George, Philipp Benner

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  19. How to accelerate the inorganic materials synthesis: from computational guidelines to data-driven method?

    National Science Review · 2025-02-27

    Yilei Wu, Xiangyang Li, Rong Guo, Rui Xu, Ming‐Gang Ju, Jinlan Wang

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  20. Mechanistically Interpretable AI for Accelerated Energy Materials Design

    Research Square · 2025-03-21

    Meilin Liu, Xueyu Hu, Ke Liao, Yucun Zhou, Haoyu Li, Zheyu Luo, Nai Shi, Yong Ling Ding, Ning Wang, Weilin Zhang, Doyeub Kim, Chanho Kim, Yoojin Ahn, Nikhil Govindarajan, Zhijun Liu

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  21. Lasso Model-Based Optimization of CNC/CNF/rGO Nanocomposites

    Micromachines · 2025-03-28

    Ghazaleh Ramezani, Ixchel Ocampo, Ion G. Stiharu, Theo G. M. van de Ven, Vahé Nerguizian

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  22. Drawing boundaries between feasible and unfeasible zeolite intergrowths using high-throughput computational screening with synthesis validation

    Nature Materials · 2025-10-20

    Kota Oishi, Koki Muraoka, Satoko Toyama, Takeshi Iwata, Takehito Seki, Naoya Shibata, Kenta Iyoki, Toru Wakihara, Tatsuya Okubo, Akira Nakayama

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  23. Machine learning-accelerated screening of all-inorganic perovskites for tandem solar cells

    Materials Today Energy · 2025-12-19

    Chong Guo, Kun Guo, Ziliang Deng, Huacheng Zhang, Kang Li, Zhiyong Liu, Bing Xie, Pu Mao, Chee Kiang Ivan Tan, Kui Yao, Francis Eng Hock Tay

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  24. Informatics for thermodynamics: Stability to synthesizability

    Elsevier eBooks · 2026-01-01

    Krishna Rajan

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  25. Optical and polaronic properties of vacancy-ordered double perovskites: A first-principles investigation

    Physical review. B./Physical review. B · 2026-01-02

    Surajit Adhikari, Ayan Chakravorty, Priya Johari

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  26. Bridging theory and experiment in materials discovery: Machine-learning-assisted prediction of synthesizable structures

    Physical review. B./Physical review. B · 2026-01-12

    YU Xin, P. Liu, Zhuohang Xie, Wenhui Mi, Pengyue Gao, Hong Jian Zhao, Jian Lv, Yanchao Wang, Yanming Ma

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  27. Do's and Don'ts When Visiting Circularly Polarized Luminescence

    Advanced Optical Materials · 2026-02-27

    Rafael G. Uceda, Sandra Míguez‐Lago, Carlos M. Cruz, Sara P. Morcillo, Luı́s Álvarez de Cienfuegos, Víctor Blanco, Araceli G. Campaña, M. Ribagorda, D. Miguel, Juan M. Cuerva

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  28. Beyond accuracy: comprehensive alignment for AI-driven molecular design

    AI for Science · 2026-04-14

    Q H Zhang, Xiang Zhuang, Chenyi Zhou, Yihang Zhu, Tong Xu, Tianhao Li, Dianbo Liu, Shengchao Liu, Keyan Ding, Michael Li, Emine Yılmaz, Haofen Wang, Huajun Chen

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  29. Mechanistically Interpretable Artificial Intelligence for Designing Oxygen Electrocatalysts

    Advanced Materials · 2026-07-21

    Xueyu Hu, Yucun Zhou, Haoyu Li, Zheyu Luo, Nai Shi, Yan Ding, Weining Wang, Weilin Zhang, Doyeub Kim, Chanho Kim, Yoojin Ahn, Nikhil Govindarajan, Minda Zou, Zhijun Liu, Yuefeng Song, Meilin Liu

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