A Spectral Analysis Method for Automated Generation of Quantum-Accurate Interatomic Potentials

TL;DR

This paper introduces the SNAP potential, a machine-learning based interatomic potential that uses bispectrum components of local atomic environments to accurately reproduce quantum mechanical energies, forces, and stresses.

Contribution

The paper presents a new linear spectral analysis method for generating quantum-accurate interatomic potentials called SNAP, utilizing bispectrum components and linear regression.

Findings

SNAP accurately reproduces quantum energies and forces.

It can be trained efficiently on large quantum data sets.

The method is applicable to solids and liquids.

Abstract

We present a new interatomic potential for solids and liquids called Spectral Neighbor Analysis Potential (SNAP). The SNAP potential has a very general form and uses machine-learning techniques to reproduce the energies, forces, and stress tensors of a large set of small configurations of atoms, which are obtained using high-accuracy quantum electronic structure (QM) calculations. The local environment of each atom is characterized by a set of bispectrum components of the local neighbor density projected on to a basis of hyperspherical harmonics in four dimensions. The bispectrum components are the same bond-orientational order parameters employed by the GAP potential [arXiv:0910.1019]. The SNAP potential, unlike GAP, assumes a linear relationship between atom energy and bispectrum components. The linear SNAP coefficients are determined using weighted least-squares linear regression against the full QM training set. This allows the SNAP potential to be fit in a robust, automated manner to large QM data sets using many bispectrum coefficients.