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RESEARCH PAPER · The Journal of Chemical Physics · 2021

Analytical gradients for molecular-orbital-based machine learning

Sebastian J. R. Lee, Tamara Husch, Feizhi Ding, Thomas F. Miller

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13 direct citing papers in this saved network. Explore the next layer with second-order citations.

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Analytical gradients for molecular-orbital-based machine learningOriginalArtificial Intelligence based Autonomous Molecular Design for Medical Therapeutic: A Perspective2Improved accuracy and transferability of molecular-orbital-based machine learning: Organics, transition-metal complexes, non-covalent interactions, and transition states51Machine learning and materials informatics approaches in the analysis of physical properties of carbon nanotubes: A review113Artificial Intelligence for Autonomous Molecular Design: A Perspective49Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning142Fast Near Ab Initio Potential Energy Surfaces Using Machine Learning25Accurate Molecular-Orbital-Based Machine Learning Energies via Unsupervised Clustering of Chemical Space18Molecular dipole moment learning via rotationally equivariant derivative kernels in molecular-orbital-based machine learning7Molecular-orbital-based machine learning for open-shell and multi-reference systems with kernel addition Gaussian process regression12Density Matrix-Based Features as Descriptors for Oxygen Reduction and Evolution Catalysts9Predicting Fluorescence Emission Wavelengths and Quantum Yields via Machine Learning34A Deep Learning-Augmented Density Functional Framework for Reaction Modeling with Chemical Accuracy6Extension of Composite Method and Machine‐Learned Electron Correlation Model to Fourth‐Period Elements0

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  1. Artificial Intelligence based Autonomous Molecular Design for Medical Therapeutic: A Perspective

    arXiv (Cornell University) · 2021-02-10

    Rajendra Prasad Joshi, Neeraj Kumar

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  2. Improved accuracy and transferability of molecular-orbital-based machine learning: Organics, transition-metal complexes, non-covalent interactions, and transition states

    The Journal of Chemical Physics · 2021-02-12

    Tamara Husch, Jiace Sun, Lixue Cheng, Sebastian J. R. Lee, Thomas F. Miller

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  3. Machine learning and materials informatics approaches in the analysis of physical properties of carbon nanotubes: A review

    Computational Materials Science · 2021-10-11

    Luis Enrique Vivanco-Benavides, Claudia Lizbeth Martínez‐González, C. Mercado-Zúñiga, C. Torres-Torres

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  4. Artificial Intelligence for Autonomous Molecular Design: A Perspective

    Molecules · 2021-11-09

    Rajendra Prasad Joshi, Neeraj Kumar

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  5. Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning

    npj Computational Materials · 2022-03-16

    Marcel F. Langer, Alex Goeßmann, Matthias Rupp

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  6. Fast Near Ab Initio Potential Energy Surfaces Using Machine Learning

    The Journal of Physical Chemistry A · 2022-06-17

    Fenris Lu, Lixue Cheng, Ryan J. DiRisio, Jacob M. Finney, Mark A. Boyer, Pattarapon Moonkaen, Jiace Sun, Sebastian J. R. Lee, J. Emiliano Deustua, Thomas F. Miller, Anne B. McCoy

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  7. Accurate Molecular-Orbital-Based Machine Learning Energies via Unsupervised Clustering of Chemical Space

    Journal of Chemical Theory and Computation · 2022-07-20

    Lixue Cheng, Jiace Sun, Thomas F. Miller

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  8. Molecular dipole moment learning via rotationally equivariant derivative kernels in molecular-orbital-based machine learning

    The Journal of Chemical Physics · 2022-08-16

    Jiace Sun, Lixue Cheng, Thomas F. Miller

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  9. Molecular-orbital-based machine learning for open-shell and multi-reference systems with kernel addition Gaussian process regression

    The Journal of Chemical Physics · 2022-10-21

    Lixue Cheng, Jiace Sun, J. Emiliano Deustua, Vignesh C. Bhethanabotla, Thomas F. Miller

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  10. Density Matrix-Based Features as Descriptors for Oxygen Reduction and Evolution Catalysts

    The Journal of Physical Chemistry C · 2023-07-31

    Maxim Secor, Alexander V. Soudackov, Sharon Hammes‐Schiffer

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  11. Predicting Fluorescence Emission Wavelengths and Quantum Yields via Machine Learning

    Journal of Chemical Information and Modeling · 2025-03-20

    Rubens C. Souza, Júlio César Duarte, Ronaldo Ribeiro Goldschmidt, Itamar Borges

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  12. A Deep Learning-Augmented Density Functional Framework for Reaction Modeling with Chemical Accuracy

    JACS Au · 2025-07-24

    Jin Xiao, Yingfeng Zhang, Bowen Li, Shuwen Zhang, Ya Gao, Wei Chen, Han Wang, John Z. H. Zhang, Tong Zhu

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  13. Extension of Composite Method and Machine‐Learned Electron Correlation Model to Fourth‐Period Elements

    Journal of Computational Chemistry · 2025-07-29

    Ryo Fujisawa, Mikito Fujinami, Hiromi Nakai

    Read original paper ↗ ·
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