Global optimization of atomic structures with gradient-enhanced Gaussian process regression

TL;DR

This paper introduces a global optimization method for atomic structures using gradient-enhanced Gaussian process regression to efficiently locate the lowest energy configurations based on DFT data.

Contribution

It presents a novel surrogate model that incorporates energy and force information to improve global optimization of atomic structures.

Findings

Gradient information enhances search efficiency

Method successfully applied to metal oxide clusters

Improves accuracy in locating global minima

Abstract

Determination of atomic structures is a key challenge in the fields of computational physics and materials science, as a large variety of mechanical, chemical, electronic, and optical properties depend sensitively on structure. Here, we present a global optimization scheme where energy and force information from density functional theory (DFT) calculations is transferred to a probabilistic surrogate model to estimate both the potential energy surface (PES) and the associated uncertainties. The local minima in the surrogate PES are then used to guide the search for the global minimum in the DFT potential. We find that adding the gradients in most cases improves the efficiency of the search significantly. The method is applied to global optimization of [Ta$_2$O$_5$]$_x$ clusters with $x=1,2,3$, and the surface structure of oxidized ZrN.