A dislocation-aware active learned interatomic potential for Bi1−xSbx
Year: 2026
Type: article
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 the topological insulator, Bi 1 − x Sb x . Starting from an initial potential, IP 0 , 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 uncertainty of any atom near the dislocation core exceeds a threshold value, the IP i is efficiently retrained (IP i → IP i + 1 ) by incorporating DFT-computed total energies and atomic forces of representative structures that include the highest-uncertainty atom and its surrounding local environment. This strategy ensures that the relaxation process maintains a low force uncertainty until the full convergence is achieved. In this work, we demonstrate the framework on a [100] edge dislocation in Bi 7 Sb 1 . Consequently, the final IP, IP f , has two capabilities: (1) it reproduces the relaxation pathway observed during the active learning process unlike the initial IP 0 , 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 “dislocation-aware” IP f .
Source: Computational Materials Science
Authors: Moon-ki Choi, Daniel Palmer, Harley T. Johnson
Institution: University of Illinois Urbana-Champaign
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,290
Awards: Track 4: Advanced CI Coordination Ecosystem: Monitoring and Measurement Services, Track 1: ACCESS Resource Allocations Marketplace and Platform Services (RAMPS), G4375474763, ACO: An Open CI Ecosystem to Advance Scientific Discovery (OpenCI), Track 3: COre National Ecosystem for CyberinfrasTructure (CONECT), G6548904153, Track 2: Customized Multi-tier Assistance, Training, and Computational Help (MATCH) for End User ACCESS to CI
OpenAlex ID: W7162958347