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Extending the accuracy of the SNAP interatomic potential form

Journal Article · · Journal of Chemical Physics
DOI:https://doi.org/10.1063/1.5017641· OSTI ID:1429723
The Spectral Neighbor Analysis Potential (SNAP) is a classical interatomic potential that expresses the energy of each atom as a linear function of selected bispectrum components of the neighbor atoms. An extension of the SNAP form is proposed that includes quadratic terms in the bispectrum components. The extension is shown to provide a large increase in accuracy relative to the linear form, while incurring only a modest increase in computational cost. The mathematical structure of the quadratic SNAP form is similar to the embedded atom method (EAM), with the SNAP bispectrum components serving as counterparts to the two-body density functions in EAM. It is also argued that the quadratic SNAP form is a special case of an artificial neural network (ANN). The effectiveness of the new form is demonstrated using an extensive set of training data for tantalum structures. Similarly to ANN potentials, the quadratic SNAP form requires substantially more training data in order to prevent overfitting, as measured by cross-validation analysis.
Research Organization:
Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
Sponsoring Organization:
USDOE; USDOE National Nuclear Security Administration (NNSA)
Grant/Contract Number:
AC04-94AL85000; NA0003525
OSTI ID:
1429723
Alternate ID(s):
OSTI ID: 1430387
Report Number(s):
SAND--2017-13691J; 659612
Journal Information:
Journal of Chemical Physics, Journal Name: Journal of Chemical Physics Journal Issue: 24 Vol. 148; ISSN 0021-9606
Publisher:
American Institute of Physics (AIP)Copyright Statement
Country of Publication:
United States
Language:
English

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Physically-informed artificial neural networks for atomistic modeling of materials text January 2018
Machine Learning Interatomic Potentials as Emerging Tools for Materials Science journal September 2019
A Critical Review of Machine Learning of Energy Materials journal January 2020
Physically informed artificial neural networks for atomistic modeling of materials journal May 2019
Recent advances and applications of machine learning in solid-state materials science journal August 2019
Fast, accurate, and transferable many-body interatomic potentials by symbolic regression journal November 2019
Comparison of different machine learning models for the prediction of forces in copper and silicon dioxide journal January 2018
Guest Editorial: Special Topic on Data-Enabled Theoretical Chemistry journal June 2018
Deep learning inter-atomic potential model for accurate irradiation damage simulations journal June 2019
Machine learning for interatomic potential models journal February 2020
Data-driven material models for atomistic simulation journal May 2019
Group-theoretical high-order rotational invariants for structural representations: Application to linearized machine learning interatomic potential journal June 2019
Machine Learning a General-Purpose Interatomic Potential for Silicon journal December 2018
Machine Learning a General-Purpose Interatomic Potential for Silicon text January 2018
Machine Learning Interatomic Potentials as Emerging Tools for Materials Science. journalarticle January 2019
Group-theoretical high-order rotational invariants for structural representations: Application to linearized machine learning interatomic potential text January 2019
Data-driven Material Models for Atomistic Simulation text January 2019
Deep learning inter-atomic potential model for accurate irradiation damage simulations text January 2019

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