发表机构
Korea University; Sogang University; Ewha Womans University; Korea Institute of Science and Technology（高丽大学; 西江大学; 梨花女子大学; 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
研究针对生成催化剂模型平板与体相连接缺失问题，提出对比表示学习模型CatRetriever，能准确检索体相候选物，还扩展其框架为吸附能目标体相发现管道，为连接催化剂生成模型与体相催化剂发现提供可扩展途径。
AI 中文摘要
逆设计是一种新兴的数据驱动范式，用于在广阔化学空间中高效探索以发现具有目标性质的新材料。在多相催化中，表面生成模型通过直接生成催化剂表面 - 吸附物结构推动了这一目标。然而，这些模型通常在平板层面运行，无法提供相应的母体体相结构，难以评估诸如形成能、表面能、晶体对称性和可合成性等与体相相关的性质。本文将平板到体相的缺失连接作为检索问题，引入了CatRetriever，一种在共享潜在空间中对齐平板和体相晶体表示的对比表示学习模型。在分布内和验证评估集上，从平板查询中，CatRetriever能以R@1 > 91%和R@3 > 98%的准确率准确检索出合理的母体体相候选物。还将其框架扩展为一个吸附能目标体相发现管道，结合体相检索、生成搜索空间扩展和吸附能分布分析。该工作流程通过与查询平板的结构兼容性及其在不同表面环境中达到目标吸附能范围的能力来评估候选物。因此，CatRetriever为连接催化剂生成模型与物理上合理且吸附能兼容的体相催化剂发现提供了一条可扩展的途径。
英文摘要
Inverse design is an emerging data-driven paradigm for efficiently navigating vast chemical spaces to discover new materials with targeted properties, and in the context of heterogeneous catalysis, surface generative models have recently advanced this goal by directly generating catalyst surface-adsorbate structures. However, these models typically operate at the slab level and do not provide the corresponding parent bulk structure, making it difficult to assess bulk-dependent properties such as formation energy, surface energy, crystallographic symmetry, and synthesizability. Here, we address this missing slab-to-bulk connection as a retrieval problem and introduce CatRetriever, a contrastive representation learning model that aligns slab and bulk crystal representations in a shared latent space. From a slab query, CatRetriever accurately retrieves plausible parent bulk candidates with R@1 > 91% and R@3 > 98% on both the in-distribution and holdout evaluation sets. We further extend the CatRetriever framework into an adsorption energy targeted bulk discovery pipeline that combines bulk retrieval, generative search space expansion, and adsorption energy distribution analysis. This workflow evaluates candidates by both structural compatibility with the query slab and their ability to access the target adsorption energy range across diverse surface environments. CatRetriever therefore provides a scalable route for connecting catalyst generative models with physically plausible and adsorption energy compatible bulk catalyst discovery.
