Machine learning for non-additive intermolecular potentials: quantum chemistry to first-principles predictions

Author:

Graham Richard S.1ORCID,Wheatley Richard J.2ORCID

Affiliation:

1. School of Mathematical Sciences, University of Nottingham, Nottingham NG7 2RD, UK

2. School of Chemistry, University of Nottingham, Nottingham NG7 2RD, UK

Abstract

Via a generally applicable method, we interpolate ab initio calculations of intermolecular interactions and produce successful first-principles predictions.

Publisher

Royal Society of Chemistry (RSC)

Subject

Materials Chemistry,Metals and Alloys,Surfaces, Coatings and Films,General Chemistry,Ceramics and Composites,Electronic, Optical and Magnetic Materials,Catalysis

Cited by 8 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A neural network-based four-body potential energy surface for parahydrogen;The Journal of Chemical Physics;2024-06-25

2. Four-Body Nonadditive Potential Energy Surface and the Fourth Virial Coefficient of Helium;Journal of Chemical & Engineering Data;2023-11-29

3. Ab Initio Calculation of Fluid Properties for Precision Metrology;Journal of Physical and Chemical Reference Data;2023-09-01

4. Parallel Implementation of Nonadditive Gaussian Process Potentials for Monte Carlo Simulations;Journal of Chemical Theory and Computation;2023-06-27

5. Combining Machine Learning with Physical Knowledge in Thermodynamic Modeling of Fluid Mixtures;Annual Review of Chemical and Biomolecular Engineering;2023-06-08

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