Artificial Intelligence Predicted OSDAs Enable Direct Synthesis of Interlayer-Expanded Zeolites

Zeolite crystallization is a metastable process under harsh conditions with poorly understood mechanisms, making the directed synthesis of specific frameworks challenging. Organic structure-directing agents (OSDAs) are key to framework control, but their discovery remains dominated by trial-and-error screening. Here, we develop a domain knowledge-informed machine learning model to predict OSDAs, which enables the successful synthesis of three novel zeolites, namely, ECNU-30, ECNU-34, and ECNU-40 (named after East China Normal University), validating the efficacy of the model. Traditional descriptor-based machine learning models exhibit limited predictive performance in screening OSDAs for unknown zeolite frameworks. Combining an end-to-end architecture with active learning, the ECNU-Zeoformer effectively overcomes this limitation, enabling more accurate prediction of OSDA-zeolite binding energies for selecting suitable OSDAs and superior generalizability to different framework topologies.

沸石结晶是一类在严苛条件下发生的亚稳过程，其反应机理尚未被充分阐明，这使得定向合成特定沸石骨架结构极具挑战。有机结构导向剂（Organic Structure-Directing Agents, OSDAs）是调控沸石骨架的关键，但目前其发现仍主要依赖试错筛选的传统方式。本研究开发了一种融合领域知识的机器学习模型用于有机结构导向剂的预测，借此成功合成了ECNU-30、ECNU-34与ECNU-40三种新型沸石（命名源自华东师范大学），验证了该模型的实际应用效能。传统基于描述符的机器学习模型在筛选未知沸石骨架对应的有机结构导向剂时，预测性能较为有限。本研究提出的ECNU-Zeoformer将端到端架构与主动学习相结合，有效克服了上述局限，能够更精准地预测有机结构导向剂与沸石的结合能，从而筛选出适配的有机结构导向剂，且对不同骨架拓扑结构具备更优异的泛化能力。