发表机构
Ewha Womans University; Sogang University; Korea University; Korea Institute of Science and Technology (KIST)（梨花女子大学; 西江大学; 高丽大学; 韩国科学技术研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对催化剂发现人力物力消耗大及现有生成模型局限性问题，提出催化剂扩散变换器CatDiT，通过学习潜在表示实现高效训练与快速采样，支持多条件设定，用于逆催化剂设计，在氮还原反应中取得良好效果，是实用可扩展的方法。
AI 中文摘要
巨大的化学设计空间以及复杂且相互依存的设计变量，使得发现具有特定性质的催化剂需耗费大量人力和资源。尽管生成模型是一种很有前景的解决方案，但现有方法通常局限于单性质条件设定或狭窄的化学空间。本文提出了催化剂扩散变换器（CatDiT），这是一个用于逆催化剂设计的统一框架，能生成从金属间合金到氧化物表面的有效且新颖的结构。通过学习压缩的潜在表示，CatDiT实现了高效训练和快速采样，同时支持对吸附质类型、结合能和催化剂类别进行同时条件设定。该模型能可靠地控制离散性质并对连续性质进行定向控制，丰富了用于特定反应催化剂发现的候选库。作为一个代表性应用，对氮还原反应（NRR）的多条件生成产生了28个满足目标活性窗口且位于纯金属*N-*H标度线之上的密度泛函理论（DFT）弛豫合金候选物，比源分布富集了约1.5倍。这些结果表明CatDiT是一种用于性质导向的催化剂逆设计和目标催化剂生成的实用且可扩展的方法。
英文摘要
The vast chemical design space and complex, interdependent design variables make catalyst discovery for targeted properties highly labor- and resource-intensive. Although generative models have emerged as a promising solution, existing approaches are generally limited to single-property conditioning or narrow chemical spaces. Here, we present Catalyst Diffusion Transformer (CatDiT), a unified framework for inverse catalyst design that generates valid and novel structures ranging from intermetallic alloys to oxide surfaces. By learning compressed latent representations, CatDiT enables efficient training and rapid sampling while supporting simultaneous conditioning on adsorbate type, binding energy, and catalyst class. The model provides reliable control of discrete properties and directional control of continuous properties, enriching candidate pools for reaction-specific catalyst discovery. As a representative application, multi-conditional generation for the nitrogen reduction reaction (NRR) yields 28 density functional theory (DFT)-relaxed alloy candidates that satisfy the target activity window and lie above the pure-metal *N-*H scaling line, corresponding to a ~1.5-fold enrichment over the source distribution. These results establish CatDiT as a practical and scalable approach for property-directed catalyst inverse design and targeted catalyst generation.
