Accuracy and transferability of GAP models for tungsten

TL;DR

This paper develops and evaluates Gaussian Approximation Potential (GAP) models for tungsten, demonstrating their accuracy and transferability in predicting properties of defects and dislocations based on DFT data.

Contribution

It introduces a new set of GAP models for tungsten, trained on DFT data, and shows how to efficiently predict defect properties using small unit cells.

Findings

GAP models accurately predict tungsten defect properties.

Models demonstrate good transferability across configurations.

Software and data are publicly available.

Abstract

We introduce interatomic potentials for tungsten in the bcc crystal phase and its defects within the Gaussian Approximation Potential (GAP) framework, fitted to a database of first principles density functional theory (DFT) calculations. We investigate the performance of a sequence of models based on databases of increasing coverage in configuration space and showcase our strategy of choosing representative small unit cells to train models that predict properties only observable using thousands of atoms. The most comprehensive model is then used to calculate properties of the screw dislocation, including its structure, the Peierls barrier and the energetics of the vacancy-dislocation interaction. All software and raw data are available at www.libatoms.org.