Atomistic structure search using local surrogate mode

TL;DR

This paper introduces a local surrogate model based on Gaussian approximation potentials to enhance atomistic structure searches, enabling transfer learning and multi-stoichiometry exploration within the Atomistic Global Optimization X framework.

Contribution

The paper presents a novel local surrogate model integrated with global search methods, improving efficiency and transferability in atomistic structure optimization.

Findings

Model is robust across various atomistic systems.

Enables transfer learning from smaller systems.

Facilitates concurrent multi-stoichiometry searches.

Abstract

We describe a local surrogate model for use in conjunction with global structure search methods. The model follows the Gaussian approximation potential (GAP) formalism and is based on a the smooth overlap of atomic positions descriptor with sparsification in terms of a reduced number of local environments using mini-batch $k$-means. The model is implemented in the Atomistic Global Optimization X framework and used as a partial replacement of the local relaxations in basin hopping structure search. The approach is shown to be robust for a wide range of atomistic system including molecules, nano-particles, surface supported clusters and surface thin films. The benefits in a structure search context of a local surrogate model are demonstrated. This includes the ability to transfer learning from smaller systems as well as the possibility to perform concurrent multi-stoichiometry searches.