Interatomic potential development for topological insulator Bi1-xSbx and its dislocation by force-following active learning

TL;DR

This paper presents a force-following active learning algorithm that combines DFT and Gaussian Approximation Potentials to develop an accurate interatomic potential for dislocations in topological insulator Bi1-xSbx, improving relaxation pathways and material properties modeling.

Contribution

The paper introduces a novel active learning approach that iteratively refines interatomic potentials during structural relaxation, specifically tailored for dislocation modeling in topological insulators.

Findings

Final potential reproduces relaxation pathways accurately.

Potential captures lattice and elastic properties across Sb concentrations.

Dislocation properties like Peierls stresses are effectively evaluated.

Abstract

We introduce a force following active learning algorithm that integrates density functional theory DFT with the Gaussian Approximation Potential GAP framework to develop a robust interatomic potential IP for a dislocation in a topological insulator Bi1xSbx. Starting from an initial potential IP0 trained on unit cell data from strained Bi Sb binaries our active learning approach iteratively refines the IP during a structural relaxation. In each cycle if the force error uncertainty of any atom near the dislocation core exceeds a threshold value the IPi is efficiently retrained IPi to IPi1 by incorporating DFT computed forces and energies of atoms near the high uncertainty atom. This strategy ensures that the relaxation process maintains a low force error until full convergence is achieved. Consequently the final IP here IP5 has two capabilities 1 it reproduces the relaxation pathway observed during the active learning process unlike the initial IP0 which lacks prior dislocation core knowledge and 2 it captures the lattice and elastic properties of Bi Sb binaries across a range of Sb concentrations. We also evaluate dislocation properties Peierls stresses and dislocation generation by compression to assess the performance of the trained potential IP5.