AI 中文总结
本研究开发机器学习模型，基于174组合成参数预测沸石骨架，识别关键参数并找到6种EMT合成新条件，5种经实验验证，为加速沸石合成提供数据驱动方案。
AI 中文摘要
沸石是具有多样骨架的微孔晶体材料，广泛应用于石油炼制、分子分离等工业领域。与大多数沸石不同，EMT沸石可在温和条件（低温且无需有机结构导向剂）下合成，因此在低成本、环境友好的生产中颇具吸引力。然而，能选择性生成EMT而非FAU等相似骨架的特定合成条件尚未明确。本研究开发机器学习（ML）模型以指导EMT沸石合成条件的发现。数据集包含174次合成实验，记录了反应时间、温度、硅源与铝源、硅铝化学计量比及其他合成参数。研究采用经典ML方法和预训练基础模型，基于这些参数预测沸石骨架产物。特征重要性分析确定了EMT形成的关键参数，验证了已知合成原理。利用ML模型探索合成空间，识别出6种有前景的EMT新合成条件。实验验证证实其中5种条件下可结晶出EMT，包括2种硅铝化学计量比超出训练数据集范围的情况。对文献报道的独立合成条件的评估进一步证明了模型的泛化能力。本研究展示了加速沸石合成的数据驱动方法，实现了ML预测与实验验证的闭环。
英文摘要
Zeolites are microporous crystalline materials with diverse frameworks, widely used in industrial applications such as petroleum refining and molecular separation. Unlike most zeolites, EMT can be synthesized under mild conditions (at low temperatures and without the use of organic structure-directing agents), making it attractive for cost-effective and environmentally sustainable production. However, the specific synthesis conditions that selectively produce EMT rather than similar frameworks like FAU are not yet well established. In this work, we develop machine learning (ML) models to guide the discovery of synthesis conditions for EMT zeolites. Our dataset comprises 174 experimental synthesis attempts, recording reaction time, temperature, silica and alumina sources, Si/Al stoichiometric ratio, and other synthesis parameters. We apply both classical ML methods and pretrained foundation models to predict zeolite framework outcomes from these synthesis parameters. Feature importance analysis identifies critical parameters for EMT formation, validating known synthesis principles. Leveraging the ML models, we explore the synthesis space and identify six promising new conditions for EMT formation. Experimental validation confirms EMT crystallization in five cases, including two with Si/Al stoichiometric ratios outside the training dataset's range. Evaluation on independent literature-reported synthesis conditions further demonstrates the generalizability of the model. This work demonstrates a data-driven approach to accelerating zeolite synthesis, closing the loop between ML prediction and experimental validation.
Journal refPhys. Rev. Materials 10, 083801, 2026
