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arXiv 2101.03530physics.chem-ph
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机器学习在化学反应中的变革性应用

Transformative Applications of Machine Learning for Chemical Reactions

M. Meuwly

摘要:

机器学习技术应用于化学反应有着悠久的历史。目前的贡献讨论的应用范围从小分子反应动力学平台的反应规划。ML为基础的技术可以是特别感兴趣的问题,涉及计算和实验。首先,贝叶斯推断是一种强大的方法,可以在改进计算模型时包含来自实验的知识。基于ML的方法也可以用于处理使用传统方法形式上难以处理的问题,例如反应性冲突中状态到状态信息的详尽表征。最后,显式模拟反应网络,因为它们发生在燃烧中已经成为可能,使用机器学习的神经网络潜力。这篇综述概述了可以并且已经使用机器学习技术解决的问题,并展望了这个多样化和刺激性领域的挑战。它的结论是,ML应用于化学问题的实践和设想今天有可能改变的方式与该领域的方法涉及化学反应的问题,无论是在研究和学术教学。

英文摘要:

Machine learning techniques applied to chemical reactions has a long history. The present contribution discusses applications ranging from small molecule reaction dynamics to platforms for reaction planning. ML-based techniques can be of particular interest for problems which involve both, computation and experiments. For one, Bayesian inference is a powerful approach to include knowledge from experiment in improving computational models. ML-based methods can also be used to handle problems that are formally intractable using conventional approaches, such as exhaustive characterization of state-to-state information in reactive collisions. Finally, the explicit simulation of reactive networks as they occur in combustion has become possible using machine-learned neural network potentials. This review provides an overview of the questions that can and have been addressed using machine learning techniques and an outlook discusses challenges in this diverse and stimulating field. It is concluded that ML applied to chemistry problems as practiced and conceived today has the potential to transform the way with which the field approaches problems involving chemical reactions, both, in research and academic teaching.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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