Zhang Xiaoqi, Jablonka Kevin Maik, Smit Berend
Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne(EPFL) Rue de l'Industrie 17 CH-1951 Sion Valais Switzerland
Laboratory of Organic and Macromolecular Chemistry (IOMC), Friedrich Schiller University Jena Humboldtstrasse 10 07743 Jena Germany.
Digit Discov. 2024 Jun 10;3(7):1410-1420. doi: 10.1039/d4dd00116h. eCollection 2024 Jul 10.
This work presents a recommendation system for metal-organic frameworks (MOFs) inspired by online content platforms. By leveraging the unsupervised Doc2Vec model trained on document-structured intrinsic MOF characteristics, the model embeds MOFs into a high-dimensional chemical space and suggests a pool of promising materials for specific applications based on user-endorsed MOFs with similarity analysis. This proposed approach significantly reduces the need for exhaustive labeling of every material in the database, focusing instead on a select fraction for in-depth investigation. Ranging from methane storage and carbon capture to quantum properties, this study illustrates the system's adaptability to various applications.
这项工作提出了一种受在线内容平台启发的金属有机框架（MOF）推荐系统。通过利用基于文档结构的MOF固有特性训练的无监督Doc2Vec模型，该模型将MOF嵌入到高维化学空间中，并通过相似性分析，根据用户认可的MOF为特定应用推荐一批有前景的材料。该方法显著减少了对数据库中每种材料进行详尽标记的需求，而是专注于一小部分进行深入研究。从甲烷储存、碳捕获到量子特性，本研究展示了该系统对各种应用的适应性。
