OxiGen: Oxidation-State-Aware Crystal Generation
To address this limitation, we propose OxiGen, an oxidation-state-aware crystal diffusion model that explicitly represents oxidation states during generation.
ProofPaper ↗
Key points
- 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.
- OxiGen enforces global charge neutrality by construction using a structured output layer with exact inference over a finite-state automaton.
Sources (1)
- [1]OxiGen: Oxidation-State-Aware Crystal GenerationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 6, 01:01 PM
To address this limitation, we propose OxiGen, an oxidation-state-aware crystal diffusion model that explicitly represents oxidation states during generation.
Generative models have the potential to accelerate inorganic materials discovery by enabling inverse design, but generating experimentally realisable crystals remains challenging.
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