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• W2997479617 abstract "Recently, machine learning (ML) methods have gained popularity and have performed as powerfully predictive tools in various areas of academic and industrious activities. In comparison, their application in catalysis has been underdeveloped. Relying on the rapid development of different algorithms and their implementation, it is the right timing to harvest the potential of ML in catalysis across academy and industry spectra. Herein, we discuss the current applications in the field of homogeneous and heterogeneous catalysis by using various ML approaches. To the best of our knowledge, modern statistical learning techniques will be a strong tool for computational optimization and discovery. This in turn will accurately extract the underlying mechanism in the model that converts readily available data and precatalysts into their promising and useful ones." @default.
• W2997479617 created "2020-01-10" @default.
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• W2997479617 date "2019-12-24" @default.
• W2997479617 modified "2023-10-11" @default.
• W2997479617 title "Machine Learning in Catalysis, From Proposal to Practicing" @default.
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• W2997479617 doi "https://doi.org/10.1021/acsomega.9b03673" @default.
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