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Machine Learning in Catalysis, From Proposal to Practicing.
ACS Omega ( IF 3.7 ) Pub Date : 2019-12-24 , DOI: 10.1021/acsomega.9b03673 Wenhong Yang 1, 2 , Timothy Tizhe Fidelis 1, 2 , Wen-Hua Sun 1, 2
ACS Omega ( IF 3.7 ) Pub Date : 2019-12-24 , DOI: 10.1021/acsomega.9b03673 Wenhong Yang 1, 2 , Timothy Tizhe Fidelis 1, 2 , Wen-Hua Sun 1, 2
Affiliation
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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.
中文翻译：
催化中的机器学习，从建议到实践。
近年来，机器学习（ML）方法已经普及并在学术和勤奋活动的各个领域中作为强大的预测工具发挥了作用。相比之下，它们在催化中的应用尚不完善。依靠不同算法及其实现的快速发展，现在是在学院和行业范围内挖掘ML催化潜力的正确时机。本文中，我们讨论了通过使用各种ML方法在均相和非均相催化领域中的当前应用。据我们所知，现代统计学习技术将是用于计算优化和发现的强大工具。
更新日期：2020-01-14
中文翻译：
催化中的机器学习，从建议到实践。
近年来，机器学习（ML）方法已经普及并在学术和勤奋活动的各个领域中作为强大的预测工具发挥了作用。相比之下，它们在催化中的应用尚不完善。依靠不同算法及其实现的快速发展，现在是在学院和行业范围内挖掘ML催化潜力的正确时机。本文中，我们讨论了通过使用各种ML方法在均相和非均相催化领域中的当前应用。据我们所知，现代统计学习技术将是用于计算优化和发现的强大工具。
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