The 66th Annual Meeting of Jpn. Petrol. Inst. (The 72nd R&D Symposium of JPI)

Presentation information

Award lecture

JPI award lectures

Tue. May 28, 2024 3:35 PM - 4:35 PM Room-C (Zuiun)

Chair:Ken Motokura(Yokohama National Univ.)

3:35 PM - 4:05 PM

[S1] Machine learning-based catalyst design and their application to C1 chemistry

○Kohji Omata1 (1. Shimane University)

Keywords:machine learning, catalyst design, C1 chemistry

Materials informatics(MI), including machine learning methods for material design, is expected to be increasingly important as an essential methodology for catalyst development, with various materials databases. In the early stages of MI, I have primary utilized support vector machine (SVM) and genetic algorithms for catalyst design. They are robust methods to improve catalyst activity, selectivity, and life by optimizing catalyst components, catalyst preparation conditions, and reaction conditions. SVM can also be used as a tool to estimate the key factors that determine catalytic activity.