Multi-fidelity Machine Learning Interatomic Potentials for Charged Point Defects

TL;DR

This paper introduces a multi-fidelity machine learning approach with charge embeddings to accurately model charged point defects in materials, achieving quantum-level precision at reduced computational costs.

Contribution

It presents a novel multi-fidelity framework with global defect charge embeddings for improved ML interatomic potentials of charged defects in semiconductors.

Findings

Accurately predicts defect charge transition levels.

Reproduces defect energetics with quantum mechanical accuracy.

Reduces computational cost compared to direct quantum calculations.

Abstract

Machine learning interatomic potentials (MLIPs) can now reproduce the energy, forces and stresses of bulk materials with high accuracy compared to first-principles calculations. The description of imperfections, where coordination environments and electron counts deviate from those found in pristine reference structures, remains a challenge. We find that the current generation of foundation MLIPs do not describe the defect physics of the semiconductor Sb2Se3. We introduce global defect charge embeddings that distinguish the bonding characteristics of different charge states. We further employ a multi-fidelity approach that combines low-cost (semi-local exchange-correlation functional) reference data with high-quality (non-local hybrid functional) energies and forces that describe well the subtleties of the defect energy landscape. The resulting defect-capable force fields can find stable structural configurations and predict charge-transition levels in quantitative agreement with direct quantum mechanical calculations, at a fraction of the computational cost.