Machine learning for interatomic potential models
Author:
Affiliation:
1. Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
Funder
Office of Naval Research
Publisher
AIP Publishing
Subject
Physical and Theoretical Chemistry,General Physics and Astronomy
Link
http://aip.scitation.org/doi/am-pdf/10.1063/1.5126336
Reference153 articles.
1. Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces
2. Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons
3. Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials
4. Moment Tensor Potentials: A Class of Systematically Improvable Interatomic Potentials
5. Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
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