English

Constrained Diffusion for Accelerated Structure Relaxation of Inorganic Solids with Point Defects

Materials Science 2026-02-24 v1 Artificial Intelligence Machine Learning

Abstract

Point defects affect material properties by altering electronic states and modifying local bonding environments. However, high-throughput first-principles simulations of point defects are costly due to large simulation cells and complex energy landscapes. To this end, we propose a generative framework for simulating point defects, overcoming the limits of costly first-principles simulators. By leveraging a primal-dual algorithm, we introduce a constraint-aware diffusion model which outperforms existing constrained diffusion approaches in this domain. Across six defect configuration settings for Bi2Te3, the proposed approach provides state-of-the-art performance generating physically grounded structures.

Keywords

Cite

@article{arxiv.2602.19153,
  title  = {Constrained Diffusion for Accelerated Structure Relaxation of Inorganic Solids with Point Defects},
  author = {Jingyi Cui and Jacob K. Christopher and Ankita Biswas and Prasanna V. Balachandran and Ferdinando Fioretto},
  journal= {arXiv preprint arXiv:2602.19153},
  year   = {2026}
}

Comments

Appeared in the NeurIPS 2025 Workshop on AI for Accelerated Material Design (AI4Mat)

R2 v1 2026-07-01T10:46:13.804Z