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RESEARCH PAPER · ACS Energy Letters · 2017

Wave Functions, Density Functionals, and Artificial Intelligence for Materials and Energy Research: Future Prospects and Challenges

Martín A. Mosquera, Bo Fu, Kevin L. Kohlstedt, George Chappell Schatz, Mark A. Ratner

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Wave Functions, Density Functionals, and Artificial Intelligence for Materials and Energy Research: Future Prospects and ChallengesOriginalFrom Static to Dynamic: Electron Density of HOMO at Biaryl Linkage Controls the Mechanism of Hole Delocalization172D Oxide Nanomaterials to Address the Energy Transition and Catalysis121Machine learning in materials design and discovery: Examples from the present and suggestions for the future198Design and Optimization of Catalysts Based on Mechanistic Insights Derived from Quantum Chemical Reaction Modeling219Machine Learning Interatomic Potentials as Emerging Tools for Materials Science1036Modified Density Functional Dispersion Correction for Inorganic Layered MFX Compounds (M = Ca, Sr, Ba, Pb and X = Cl, Br, I)8Data-Based Methods for Materials Design and Discovery: Basic Ideas and General Methods10Artificial intelligence for photonics and photonic materials63Critical review of machine learning applications in perovskite solar research139Machine Learning‐Aided Crystal Facet Rational Design with Ionic Liquid Controllable Synthesis34Role of artificial intelligence in chemistry26Application of Advanced Vibrational Spectroscopy in Revealing Critical Chemical Processes and Phenomena of Electrochemical Energy Storage and Conversion27Instantaneous Property Prediction and Inverse Design of Plasmonic Nanostructures Using Machine Learning: Current Applications and Future Directions32Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery552Machine Learning Identifies Strong Electronic Contacts in Semiconducting Polymer Melts10Recent progress in the design and application of machine learning for the hydrogen evolution reaction in electrocatalysis and photocatalysis3

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  1. From Static to Dynamic: Electron Density of HOMO at Biaryl Linkage Controls the Mechanism of Hole Delocalization

    Journal of the American Chemical Society · 2018-03-29

    Maxim V. Ivanov, Denan Wang, Rajendra Rathore

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  2. 2D Oxide Nanomaterials to Address the Energy Transition and Catalysis

    Advanced Materials · 2018-08-22

    Christopher James Heard, Jiřı́ Čejka, Maksym Opanasenko, Petr Nachtigall, Gabriele Centi, Siglinda Perathoner

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  3. Machine learning in materials design and discovery: Examples from the present and suggestions for the future

    Physical Review Materials · 2018-12-20

    James E. Gubernatis, Turab Lookman

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  4. Design and Optimization of Catalysts Based on Mechanistic Insights Derived from Quantum Chemical Reaction Modeling

    Chemical Reviews · 2019-05-08

    Seihwan Ahn, Mannkyu Hong, Mahesh Sundararajan, Daniel H. Ess, Mu‐Hyun Baik

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  5. Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

    Advanced Materials · 2019-09-05

    Volker L. Deringer, Miguel A. Caro, Gábor Cśanyi

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  6. Modified Density Functional Dispersion Correction for Inorganic Layered MFX Compounds (M = Ca, Sr, Ba, Pb and X = Cl, Br, I)

    The Journal of Physical Chemistry A · 2020-01-30

    Daniel Sethio, João B. L. Martins, Latévi Max Lawson Daku, Hans R. Hagemann, Elfi Kraka

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  7. Data-Based Methods for Materials Design and Discovery: Basic Ideas and General Methods

    Synthesis lectures on materials and optics · 2020-03-06

    Ghanshyam Pilania, Prasanna V. Balachandran, James E. Gubernatis, Turab Lookman

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  8. Artificial intelligence for photonics and photonic materials

    Reports on Progress in Physics · 2020-09-02

    Davide Piccinotti, Kevin F. MacDonald, Simon A. Gregory, Ian J. Youngs, Nikolay I. Zheludev

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  9. Critical review of machine learning applications in perovskite solar research

    Nano Energy · 2020-10-28

    Beyza Yılmaz, Ramazan Yıldırım

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  10. Machine Learning‐Aided Crystal Facet Rational Design with Ionic Liquid Controllable Synthesis

    Small · 2021-03-01

    Fuming Lai, Zhehao Sun, Sandra Elizabeth Saji, Yichuan He, Xue‐Feng Yu, Haitao Zhao, Haibo Guo, Zongyou Yin

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  11. Role of artificial intelligence in chemistry

    Materials Today Proceedings · 2021-10-20

    Neeru Choudhary, Ruchi Bharti, Renu Sharma

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  12. Application of Advanced Vibrational Spectroscopy in Revealing Critical Chemical Processes and Phenomena of Electrochemical Energy Storage and Conversion

    ACS Applied Materials & Interfaces · 2022-02-07

    You Wang, Dongchang Chen

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  13. Instantaneous Property Prediction and Inverse Design of Plasmonic Nanostructures Using Machine Learning: Current Applications and Future Directions

    Nanomaterials · 2022-02-14

    Xinkai Xu, Dipesh Aggarwal, Karthik Shankar

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  14. Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery

    Chemical Reviews · 2022-07-21

    Haoxin Mai, Tu C. Le, Dehong Chen, David Alan Winkler, Rachel A. Caruso

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  15. Machine Learning Identifies Strong Electronic Contacts in Semiconducting Polymer Melts

    Macromolecules · 2023-07-21

    Puja Agarwala, Shane Donaher, Baskar Ganapathysubramanian, Enrique Daniel Gomez, Scott T. Milner

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  16. Recent progress in the design and application of machine learning for the hydrogen evolution reaction in electrocatalysis and photocatalysis

    Materials Today Communications · 2025-04-08

    Kaifeng Zhang, Xudong Wang, Yanjing Su

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