发表机构
Imperial College London（帝国理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出氧化态感知的晶体扩散模型OxiGen，显式表示氧化态并通过结构化输出层保证电荷中性，显著提升氧化态保真度与晶体生成质量。
AI 中文摘要
生成模型通过实现逆向设计有望加速无机材料的发现，但生成实验上可实现的晶体仍然具有挑战性。氧化态被广泛用于评估晶体的成分有效性并指导无机材料的发现。虽然现有的晶体生成模型能够生成具有电荷中性氧化态分配的晶体，但它们难以再现合成材料中观察到的氧化态分布。为解决这一局限性，我们提出了OxiGen，一种氧化态感知的晶体扩散模型，在生成过程中显式表示氧化态。OxiGen通过使用具有有限状态自动机上精确推理的结构化输出层，从构造上强制实现全局电荷中性。实验上，OxiGen显著提高了氧化态保真度，在评估的方法中生成最高比例的稳定、独特且新颖的晶体，并且即使在属性条件下也保持高成分有效性。
英文摘要
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.
Comments27 pages, 4 figures
