Proper Orthogonal Descriptors for Efficient and Accurate Interatomic Potentials
arXiv:2209.02362 · doi:10.1016/j.jcp.2023.112030
Abstract
We present the proper orthogonal descriptors for efficient and accuracy representation of the potential energy surface. The potential energy surface is represented as a many-body expansion of parametrized potentials in which the potentials are functions of atom positions and parameters. The Karhunen-Loève (KL) expansion is employed to decompose the parametrized potentials into a set of proper orthogonal descriptors (PODs). Because of the rapid convergence of the KL expansion, relevant snapshots can be sampled exhaustively to represent the atomic neighborhood environment accurately with a small number of descriptors. The proper orthogonal descriptors are used to develop interatomic potentials by using a linear expansion of the descriptors and determining the expansion coefficients from a weighted least-squares regression against a density functional theory (DFT) training set. We present a comprehensive evaluation of the POD potentials on previously published DFT data sets comprising Li, Mo, Cu, Ni, Si, Ge, and Ta elements. The data sets represent a diverse pool of metals, transition metals, and semiconductors. The accuracy of the POD potentials are comparable to that of state-of-the-art machine learning potentials such as the spectral neighbor analysis potential (SNAP) and the atomic cluster expansion (ACE).
arXiv comment37 pages, 8 figures, 5 tables. arXiv admin note: text overlap with arXiv:1906.08888 by other authors
References in corpus (14)
- Gaussian Approximation Potentials: the accuracy of quantum mechanics, without the electrons
- On representing chemical environments
- Moment Tensor Potentials: a class of systematically improvable interatomic potentials
- A Spectral Analysis Method for Automated Generation of Quantum-Accurate Interatomic Potentials
- A Performance and Cost Assessment of Machine Learning Interatomic Potentials
- Machine Learning Unifies the Modelling of Materials and Molecules
- Machine-learning based interatomic potential for amorphous carbon
- Machine learning a general purpose interatomic potential for silicon
- Environment Dependent Interatomic Potential for Bulk Silicon
- Achieving DFT accuracy with a machine-learning interatomic potential: thermomechanics and defects in bcc ferromagnetic iron
- Extending the Accuracy of the SNAP Interatomic Potential Form
- Atomic cluster expansion of scalar, vectorial and tensorial properties and including magnetism and charge transfer
- Gaussian approximation potential modeling of lithium intercalation in carbon nanostructures
- Explicit Multi-element Extension of the Spectral Neighbor Analysis Potential for Chemically Complex Systems