Machine Learning for Catalysis Informatics: Recent Applications and Prospects
ACS Catalysis(2020)
摘要
The discovery and development of catalysts and catalytic processes are essential components to maintaining an ecological balance in the future. Recent revolutions made in data science could have a great impact on traditional catalysis research in both industry and academia and could accelerate the development of catalysts. Machine learning (ML), a subfield of data science, can play a central role in this paradigm shift away from the use of traditional approaches. In this review, we present a user’s guide for ML that we believe will be helpful for scientists performing research in the field of catalysis and summarize recent progress that has been made in utilizing ML to create homogeneous and heterogeneous catalysts. The focus of the review is on the design, synthesis, and characterization of catalytic materials/compounds as well as their applications to catalyzed processes. The ML technique not only enhances ways to discover catalysts but also serves as a powerful tool to establish a deeper understanding ...
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关键词
machine learning,catalysis informatics,high-throughput experiments/computations,data mining,structure-activity relationships
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