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arXiv · 2605.16611

Machine Learning Approaches to Point Defects in Non-Metallic Materials: A Review of Methods

Yu Kumagai·Shin Kiyohara
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Abstract

We review recent machine-learning (ML) approaches for point defects in non-metallic materials, with an emphasis on defect formation energies. Existing studies largely fall into two categories: direct ML models that predict defect energetics from local structural representations, and machine-learning potentials (MLPs) that approximate the defect-containing potential energy surface. We summarize key achievements as well as persistent bottlenecks, emphasizing that dataset quality often dominates practical model performance. We further identify charged-defect formation energies as a central frontier, where Fermi-level alignment, finite-size corrections, and long-range electrostatics must be handled carefully and consistently to enable meaningful comparisons and transferable predictions across different materials.

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Yu Kumagai, Shin Kiyohara. 2026-05-15. Machine Learning Approaches to Point Defects in Non-Metallic Materials: A Review of Methods. https://arxiv.org/abs/2605.16611

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