Department of Chemistry, Hokkaido University, North 10, West 8, Sapporo 060-0810, Japan.
Faculty of Advanced Science and Technology, Kumamoto University, 2-39-1 Kurokami, Chuo-ku, 860-8555, Japan.
Chem Commun (Camb). 2023 Feb 21;59(16):2222-2238. doi: 10.1039/d2cc05938j.
Designing catalysts is a challenging matter as catalysts are involved with various factors that impact synthesis, catalysts, reactor and reaction. In order to overcome these difficulties, catalysts informatics is proposed as an alternative way to design and understand catalysts. The underlying concept of catalysts informatics is to design the catalysts from trends and patterns found in catalysts data. Here, three key concepts are introduced: experimental catalysts database, knowledge extraction from catalyst data data science, and a catalysts informatics platform. Methane oxidation is chosen as a prototype reaction for demonstrating various aspects of catalysts informatics. This work summarizes how catalysts informatics plays a role in catalyst design. The work covers big data generation high throughput experiments, machine learning, catalysts network method, catalyst design from small data, catalysts informatics platform, and the future of catalysts informatics ontology. Thus, the proposed catalysts informatics would help innovate how catalysts can be designed and understood.
设计催化剂是一项具有挑战性的任务,因为催化剂涉及到许多影响合成、催化剂、反应器和反应的因素。为了克服这些困难,提出了催化剂信息学作为设计和理解催化剂的一种替代方法。催化剂信息学的基本概念是从催化剂数据中的趋势和模式设计催化剂。这里介绍了三个关键概念:实验催化剂数据库、从催化剂数据中提取知识(数据科学)以及催化剂信息学平台。甲烷氧化被选为示范催化剂信息学各个方面的原型反应。这项工作总结了催化剂信息学在催化剂设计中的作用。这项工作涵盖了大数据生成(高通量实验)、机器学习、催化剂网络方法、从少量数据设计催化剂、催化剂信息学平台以及催化剂信息学的未来(本体论)。因此,所提出的催化剂信息学将有助于创新催化剂的设计和理解方式。