催化材料研究者终于有了一个能同时做性质预测和逆向设计的统一模型，避免了传统解耦方法的数据偏移问题，做材料计算和 AI 辅助设计的团队可以直接参考其方法。
CatalyticMLLM 是一种用于催化材料的图-文本多模态大语言模型，将性质预测和逆向结构设计统一在同一模型和共享表示空间中。传统方法将这两个任务解耦，导致生成模型与预测模型之间存在表示空间和训练目标的不一致，容易引入数据分布偏移和评估偏差。该模型通过统一框架，不仅能利用三维结构和文本信息进行可靠的性质预测，还能根据目标性质生成并筛选物理可行的 CIF 候选结构，形成“逆向设计-预测-筛选-重新设计”的闭环优化。实验表明，该统一范式在催化松弛能预测和逆向设计任务上均优于解耦基线，验证了联合建模的有效性。
CatalyticMLLM: A Graph-Text Multimodal Large Language Model for Catalytic Materials
Property prediction and inverse structural design of catalytic materials are typically modeled as two independent tasks: the former predicts target properties from given structures, whereas the latter generates candidate structures according to desired properties. Although the decoupled paradigm facilitates the implementation of a ``generation--evaluation--screening'' workflow, the inconsistency between the generative model and the property prediction model in terms of representation spaces and training objectives can readily introduce data distribution shifts and evaluator bias, thereby limiting the stability of closed-loop optimization. In this work, we propose QE-Catalytic-V2, a unified graph--text multimodal large language model for catalytic materials, which integrates property prediction and inverse design within the same model and shared representation space. Under this unified framework, QE-Catalytic-V2 can not only perform reliable property prediction by leveraging three-dimensional structures and textual information, but also generate and screen physically feasible CIF candidates conditioned on target properties, thereby forming a closed-loop optimization workflow of ``inverse design--prediction--screening--redesign.'' Experimental results demonstrate that this unified paradigm outperforms decoupled baselines on both catalytic relaxed-energy prediction and inverse design tasks, validating the effectiveness of jointly modeling property prediction and structure generation within a single multimodal model.