Atomistic Structure Learning Algorithm with surrogate energy model relaxation

TL;DR

This paper enhances the Atomistic Structure Learning Algorithm (ASLA) by integrating a surrogate energy model to enable efficient structural relaxations, significantly improving its performance in surface structure determination.

Contribution

The work introduces a surrogate energy model within ASLA, allowing for approximate relaxations and reducing computational costs during structure searches.

Findings

Improved performance in benzene structure building.

Successful identification of a surface reconstruction on Ag(111).

Enhanced efficiency in structure determination processes.

Abstract

The recently proposed Atomistic Structure Learning Algorithm (ASLA) builds on neural network enabled image recognition and reinforcement learning. It enables fully autonomous structure determination when used in combination with a first-principles total energy calculator, e.g. a density functional theory (DFT) program. To save on the computational requirements, ASLA utilizes the DFT program in a single-point mode, i.e. without allowing for relaxation of the structural candidates according to the force information at the DFT level. In this work, we augment ASLA to establish a surrogate energy model concurrently with its structure search. This enables approximative but computationally cheap relaxation of the structural candidates before the single-point energy evaluation with the computationally expensive DFT program. We demonstrate a significantly increased performance of ASLA for building benzene while utilizing a surrogate energy landscape. Further we apply this model-enhanced ASLA in a thorough investigation of the c(4x8) phase of the Ag(111) surface oxide. ASLA successfully identifies a surface reconstruction which has previously only been guessed on the basis of scanning tunnelling microscopy images.