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RESEARCH PAPER · ChemRxiv · 2020

The ANI-1ccx and ANI-1x Data Sets, Coupled-Cluster and Density Functional Theory Properties for Molecules

Justin S. Smith, R.I. Zubatyuk, Benjamin T. Nebgen, Nicholas Lubbers, Kipton M. Barros, Adrián E. Roitberg, Olexandr Isayev, Sergei Tretiak

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

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42 direct citing works

The ANI-1ccx and ANI-1x Data Sets, Coupled-Cluster and Density Functional Theory Properties for MoleculesOriginalGround State Energy Functional with Hartree–Fock Efficiency and Chemical Accuracy72Linear Atomic Cluster Expansion Force Fields for Organic Molecules: beyond RMSE0Linear Atomic Cluster Expansion Force Fields for Organic Molecules: beyond RMSE6Linear Atomic Cluster Expansion Force Fields for Organic Molecules: beyond RMSE1Linear Atomic Cluster Expansion Force Fields for Organic Molecules: beyond RMSE6Quantum Mechanics Enables "Freedom of Design" in Molecular Property Space1Δ-Quantum machine learning for medicinal chemistry0Modeling Electronic Response Properties with an Explicit-Electron Machine Learning Potential29Accurate Deep Learning-Aided Density-Free Strategy for Many-Body Dispersion-Corrected Density Functional Theory26Neural Scaling of Deep Chemical Models20Transferable machine learning interatomic potential for bond dissociation energy prediction of drug-like molecule1Leveraging Multitask Learning to Improve the Transferability of Machine Learned Force Fields10Transferable Machine Learning Interatomic Potential for Bond Dissociation Energy Prediction of Drug-like Molecules1Aquamarine: Quantum-Mechanical Exploration of Conformers and Solvent Effects in Large Drug-like Molecules1Training a Machine Learned Potential on the Cerebras Wafer Scale Engine0

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  1. Ground State Energy Functional with Hartree–Fock Efficiency and Chemical Accuracy

    The Journal of Physical Chemistry A · 2020-08-03

    Yixiao Chen, Linfeng Zhang, Han Wang, E Weinan

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  2. Linear Atomic Cluster Expansion Force Fields for Organic Molecules: beyond RMSE

    Unknown journal · 2021-07-01

    Dávid Péter Kovács, Cas van der Oord, Jiří Kučera, Alice E. A. Allen, Daniel J. Cole, Christoph Ortner, Gábor Cśanyi

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  3. Linear Atomic Cluster Expansion Force Fields for Organic Molecules: beyond RMSE

    Unknown journal · 2021-07-19

    Dávid Péter Kovács, Cas van der Oord, Jiri Kucera, Alice E. A. Allen, Daniel J. Cole, Christoph Ortner, Gábor Cśanyi

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  4. Linear Atomic Cluster Expansion Force Fields for Organic Molecules: beyond RMSE

    Unknown journal · 2021-07-19

    Dávid Péter Kovács, Cas van der Oord, Jiří Kučera, Alice E. A. Allen, Daniel J. Cole, Christoph Ortner, Gábor Cśanyi

    Read original paper ↗ ·

  5. Linear Atomic Cluster Expansion Force Fields for Organic Molecules: beyond RMSE

    Unknown journal · 2021-10-12

    Dávid Péter Kovács, Cas van der Oord, Jiří Kučera, Alice E. A. Allen, Daniel J. Cole, Christoph Ortner, Gábor Cśanyi

    Read original paper ↗ ·

  6. Quantum Mechanics Enables "Freedom of Design" in Molecular Property Space

    ChemRxiv · 2021-10-18

    Leonardo Rafael Medrano Sandonas, Johannes Hoja, Brian G. Ernst, Álvaro Vázquez‐Mayagoitia, Robert A. DiStasio, Alexandre Tkatchenko

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  7. Δ-Quantum machine learning for medicinal chemistry

    Unknown journal · 2021-12-20

    Kenneth Atz, Clemens Isert, Markus N. A. Böcker, José Jiménez-Luna, Gisbert Schneider

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  8. Modeling Electronic Response Properties with an Explicit-Electron Machine Learning Potential

    Journal of Chemical Theory and Computation · 2022-02-16

    Maarten Cools‐Ceuppens, Joni Dambre, Toon Verstraelen

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  9. Accurate Deep Learning-Aided Density-Free Strategy for Many-Body Dispersion-Corrected Density Functional Theory

    The Journal of Physical Chemistry Letters · 2022-05-11

    Pier Paolo Poier, Théo Jaffrelot Inizan, Olivier Adjoua, Louis Lagardère, Jean‐Philip Piquemal

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  10. Neural Scaling of Deep Chemical Models

    ChemRxiv · 2022-05-16

    Nathan C. Frey, Ryan Soklaski, Simon Axelrod, Siddharth Samsi, Rafael Gómez‐Bombarelli, Connor W. Coley, Vijay N. Gadepally

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  11. Transferable machine learning interatomic potential for bond dissociation energy prediction of drug-like molecule

    ChemRxiv · 2023-06-29

    Elena Gelžinytė, Mario Öeren, Matthew Segall, Gábor Cśanyi

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  12. Leveraging Multitask Learning to Improve the Transferability of Machine Learned Force Fields

    ChemRxiv · 2023-09-27

    Leif D. Jacobson, James Stevenson, Farhad Ramezanghorbani, Steven Dajnowicz, Karl Leswing

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  13. Transferable Machine Learning Interatomic Potential for Bond Dissociation Energy Prediction of Drug-like Molecules

    ChemRxiv · 2023-12-13

    Elena Gelžinytė, Mario Öeren, Matthew Segall, Gábor Cśanyi

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  14. Aquamarine: Quantum-Mechanical Exploration of Conformers and Solvent Effects in Large Drug-like Molecules

    ChemRxiv · 2024-02-21

    Leonardo Rafael Medrano Sandonas, Dries Van Rompaey, Alessio Fallani, Mathias Hilfiker, David F. Hahn, Laura Pérez‐Benito, Jonas Verhoeven, Gary Tresadern, Jörg K. Wegner, Hugo Ceulemans, Alexandre Tkatchenko

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  15. Training a Machine Learned Potential on the Cerebras Wafer Scale Engine

    Unknown journal · 2025-07-18

    Dana O’Connor, Wissam Abdo Saidi, Paola A. Buitrago

    Read original paper ↗ ·
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