OxiGen: Oxidation-State-Aware Crystal Generation

Abstract:Generative models have the potential to accelerate inorganic materials discovery by enabling inverse design, but generating experimentally realisable crystals remains challenging. Oxidation states are widely used to assess the compositional validity of crystals and guide inorganic materials discovery. While existing generative models for crystals can generate materials with charge-neutral oxidation-state assignments, they poorly reproduce the distributions of oxidation states observed in synthesised materials. To address this limitation, we propose OxiGen, an oxidation-state-aware crystal diffusion model that explicitly represents oxidation states during generation. OxiGen enforces global charge neutrality by construction using a structured output layer with exact inference over a finite-state automaton. Empirically, OxiGen substantially improves oxidation-state fidelity, generates the highest rate of stable, unique, and novel crystals among evaluated methods, and maintains high compositional validity even under property conditioning.

Machine Learning (cs.LG); Materials Science (cond-mat.mtrl-sci)

arXiv:2610.08296 [cs.LG]

(or arXiv:2610.08296v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.08296 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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