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[2602.19153] Constrained Diffusion for Accelerated Structure Relaxation of Inorganic Solids with Point Defects
Summary
This article presents a novel generative framework for simulating point defects in inorganic solids, enhancing structure relaxation processes through a constraint-aware diffusion model that outperforms traditional methods.
Why It Matters
Understanding point defects is crucial for improving material properties in various applications, including electronics and energy storage. This research provides a more efficient simulation method, potentially accelerating advancements in materials science and engineering.
Key Takeaways
- Introduces a generative framework for simulating point defects.
- Utilizes a constraint-aware diffusion model for improved performance.
- Demonstrates state-of-the-art results in Bi2Te3 defect configurations.
- Addresses the limitations of high-throughput first-principles simulations.
- Aims to enhance the understanding of material properties affected by defects.
Condensed Matter > Materials Science arXiv:2602.19153 (cond-mat) [Submitted on 22 Feb 2026] Title:Constrained Diffusion for Accelerated Structure Relaxation of Inorganic Solids with Point Defects Authors:Jingyi Cui, Jacob K. Christopher, Ankita Biswas, Prasanna V. Balachandran, Ferdinando Fioretto View a PDF of the paper titled Constrained Diffusion for Accelerated Structure Relaxation of Inorganic Solids with Point Defects, by Jingyi Cui and 4 other authors View PDF HTML (experimental) 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. Comments: Subjects: Materials Science (cond-mat.mtrl-sci); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2602.19153 [cond-mat.mtrl-sci] (or arXiv:2602.19153v1 [cond-mat.mtrl-sci] for this version) https://doi.org/10.48550/arXiv.2602.19153 Focus to lear...
