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RESEARCH PAPER · Journal of Chemical Theory and Computation · 2022

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

Lixue Cheng, Jiace Sun, Thomas F. Miller

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

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42 direct citing works

Accurate Molecular-Orbital-Based Machine Learning Energies via Unsupervised Clustering of Chemical SpaceOriginalMolecular 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 regression12Neural network potentials for reactive chemistry: CASPT2 quality potential energy surfaces for bond breaking13Accurate Rate Calculations for Hydrogen Atom Abstraction from Methane by Hydroxyl Radical: A Combination of RPMD, Machine Learning Potentials, and Active Learning0Low-Data Deep Quantum Chemical Learning for Accurate MP2 and Coupled-Cluster Correlations12Learning Intermolecular Electronic Coupling from a Molecular-Orbital-Pair-based Descriptor0Divide-and-conquer potentials enable scalable and accurate predictions of forces and energies in atomistic systems6Certain Investigations on Machine Learning Models for Material Processing0SPAHM(a,b): Encoding the Density Information from Guess Hamiltonian in Quantum Machine Learning Representations10Accurate Electronic and Optical Properties of Organic Doublet Radicals Using Machine Learned Range-Separated Functionals14Making quantum chemistry compressive and expressive: Toward practical ab‐initio simulation4Machine Learning Isotropic g Values of Radical Polymers5Learning Intermolecular Electronic Coupling with Molecular-Orbital-Based Descriptors7Accurate Neural Network Fine-Tuning Approach for Transferable Ab Initio Energy Prediction across Varying Molecular and Crystalline Scales8RT-Pred: A web server for accurate, customized liquid chromatography retention time prediction of chemicals13Transferability Across Different Molecular Systems and Levels of Theory with the Data-Driven Coupled-Cluster Scheme5DDCCNet: Physics-Enhanced Multitask Neural Networks for Data-Driven Coupled-Cluster1Integrating Coupled‐Cluster Theory Within AI Workflows for Accurate Molecular Geometries0

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18 direct citing papers saved.

  1. 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

    Read original paper ↗ ·

  2. 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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  3. Neural network potentials for reactive chemistry: CASPT2 quality potential energy surfaces for bond breaking

    Digital Discovery · 2023-01-01

    Quin Hu, Andrew Johannesen, Daniel S. Graham, Jason D. Goodpaster

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  4. Accurate Rate Calculations for Hydrogen Atom Abstraction from Methane by Hydroxyl Radical: A Combination of RPMD, Machine Learning Potentials, and Active Learning

    ChemRxiv · 2023-05-04

    Adrian Gordon, Jason D. Goodpaster

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  5. Low-Data Deep Quantum Chemical Learning for Accurate MP2 and Coupled-Cluster Correlations

    Journal of Chemical Theory and Computation · 2023-07-28

    Wai-Pan Ng, Qiujiang Liang, Jun Yang

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  6. Learning Intermolecular Electronic Coupling from a Molecular-Orbital-Pair-based Descriptor

    ChemRxiv · 2023-10-09

    Jingheng Deng, Shuming Bai

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  7. Divide-and-conquer potentials enable scalable and accurate predictions of forces and energies in atomistic systems

    Digital Discovery · 2023-11-15

    Claudio Zeni, Andrea Anelli, Aldo Glielmo, Stefano de Gironcoli, Kevin Rossi

    Read original paper ↗ ·

  8. Certain Investigations on Machine Learning Models for Material Processing

    Springer proceedings in physics · 2024-01-01

    D Aarthi, S. Kannimuthu

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  9. SPAHM(a,b): Encoding the Density Information from Guess Hamiltonian in Quantum Machine Learning Representations

    Journal of Chemical Theory and Computation · 2024-01-16

    Ksenia R. Briling, Yannick Calvino Alonso, Alberto Fabrizio, Clémence Corminbœuf

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  10. Accurate Electronic and Optical Properties of Organic Doublet Radicals Using Machine Learned Range-Separated Functionals

    The Journal of Physical Chemistry A · 2024-02-21

    Cheng‐Wei Ju, Yili Shen, Ethan J. French, Jun Yi, Hongshan Bi, Aaron Tian, Zhou Lin

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  11. Making quantum chemistry compressive and expressive: Toward practical ab‐initio simulation

    Wiley Interdisciplinary Reviews Computational Molecular Science · 2024-03-01

    Jun Yang

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  12. Machine Learning Isotropic g Values of Radical Polymers

    Journal of Chemical Theory and Computation · 2024-03-08

    Davis Thomas Daniel, Souvik Mitra, Rüdiger‐A. Eichel, Diddo Diddens, Josef Granwehr

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  13. Learning Intermolecular Electronic Coupling with Molecular-Orbital-Based Descriptors

    The Journal of Physical Chemistry Letters · 2024-12-16

    Jingheng Deng, Jiayi Liang, Shuming Bai

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  14. Accurate Neural Network Fine-Tuning Approach for Transferable Ab Initio Energy Prediction across Varying Molecular and Crystalline Scales

    Journal of Chemical Theory and Computation · 2025-02-04

    Wai-Pan Ng, Zili Zhang, Jun Yang

    Read original paper ↗ ·

  15. RT-Pred: A web server for accurate, customized liquid chromatography retention time prediction of chemicals

    Journal of Chromatography A · 2025-02-25

    Mahi Zakir, Marcia LeVatte, David Scott Wishart

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  16. Transferability Across Different Molecular Systems and Levels of Theory with the Data-Driven Coupled-Cluster Scheme

    The Journal of Physical Chemistry A · 2025-03-25

    P. D. Varuna S. Pathirage, Brody Quebedeaux, Shahzad Akram, Konstantinos D. Vogiatzis

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  17. DDCCNet: Physics-Enhanced Multitask Neural Networks for Data-Driven Coupled-Cluster

    Journal of Chemical Theory and Computation · 2026-07-20

    P. D. Varuna S. Pathirage, Konstantinos D. Vogiatzis

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  18. Integrating Coupled‐Cluster Theory Within AI Workflows for Accurate Molecular Geometries

    Journal of Computational Chemistry · 2026-08-03

    Shahzad Akram, Konstantinos D. Vogiatzis

    Read original paper ↗ ·
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