This article is part of the Machine Learning at the Atomic Scale special issue. Michele Ceriotti is Associate Professor at the Institute of Materials at the École Polytechnique Fédérale de Lausanne. He received his Ph.D. in Physics from ETH Zürich in 2010, under the supervision of Professor Michele Parrinello. He spent three years in Oxford as a Junior Research Fellow at Merton College and joined EPFL in 2013, where he leads the laboratory for Computational Science and Modeling. His research interests focus on the development of methods for molecular dynamics and the simulation of complex systems at the atomistic level, as well as their application to problems in chemistry and materials science—using machine learning both as an engine to drive more accurate and predictive simulations and as a conceptual tool to investigate the interplay between data-driven and physics-inspired modeling. Cecilia Clementi is Einstein Professor of Physics at Freie Universität (FU) Berlin, Germany. She joined the faculty of FU in June 2020 after 19 years as a Professor of Chemistry at Rice University in Houston, Texas. Cecilia obtained her Ph.D. in Physics at SISSA and was a postdoctoral fellow at the University of California, San Diego, where she was part of the La Jolla Interfaces in Science program. Her research focuses on the development and application of methods for the modeling of complex biophysical processes, by means of molecular dynamics, statistical mechanics, coarse-grained models, experimental data, and machine learning. Cecilia’s research has been recognized by a National Science Foundation CAREER Award (2004) and the Robert A. Welch Foundation Norman Hackerman Award in Chemical Research (2009). Since 2016 she has also been a co-Director of the National Science Foundation Molecular Sciences Software Institute. O. Anatole von Lilienfeld is a full university professor of computational materials discovery at the Faculty of Physics at the University of Vienna. Research in his laboratory deals with the development of improved methods for a first-principles-based understanding of chemical compound space using perturbation theory, machine learning, and high-performance computing. Previously, he was an associate and assistant professor at the University of Basel, Switzerland, and at the Free University of Brussels, Belgium. From 2007 to 2013, he worked for Argonne and Sandia National Laboratories after postdoctoral studies with Mark Tuckerman at New York University and at the Institute for Pure and Applied Mathematics at the University of California Los Angeles. In 2005, he was awarded a Ph.D. in computational chemistry from EPF Lausanne under the guidance of Ursula Rothlisberger. His diploma thesis work was done at ETH Zurich with Martin Quack and the University of Cambridge with Nicholas Handy. He studied chemistry at ETH Zurich, the Ecole de Chimie Polymers et Materiaux in Strasbourg, and the University of Leipzig. He serves as editor in chief of the IOP journal Machine Learning: Science and Technology and on the editorial board of Science Advances. He has been on the editorial board of Nature’s Scientific Data from 2014 to 2019. He was the chair of the long IPAM “UCLA program ‘Navigating Chemical Compound Space for Materials and Bio Design’” which took place in 2011. He is the recipient of multiple awards including the Swiss National Science foundation postdoctoral grant (2005), Harry S. Truman postdoctoral fellowship (2007), Thomas Kuhn Paradigm Shift award (2013), Swiss National Science professor fellowship (2013), Odysseus grant from Flemish Science foundation (2016), ERC consolidator grant (2017), and Feynman Prize in Nanotechnology (2018). We would like to thank all authors of the reviews, the reviewers who ensured the clarity and scholarship of each contribution, and the editors of Chemical Reviews for their support and patience in preparing this collection. In memoriam Alessandro de Vita (1965–2018). This article has not yet been cited by other publications.

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简介：原子尺度的机器学习

本文是部分 原子尺度的机器学习特刊。Michele Ceriotti 是洛桑联邦理工学院材料研究所的副教授。他获得了博士学位。在 Michele Parrinello 教授的指导下，于 2010 年获得苏黎世联邦理工学院物理学博士学位。他在牛津大学默顿学院担任初级研究员三年，并于 2013 年加入 EPFL，领导计算科学与建模实验室。他的研究兴趣集中在分子动力学方法的开发和原子级复杂系统的模拟，以及它们在化学和材料科学问题中的应用——使用机器学习作为引擎来推动更准确和预测的模拟并作为一种概念工具来研究数据驱动和物理启发建模之间的相互作用。Cecilia Clementi 是德国柏林自由大学 (FU) 的爱因斯坦物理学教授。在德克萨斯州休斯敦莱斯大学担任化学教授 19 年后，她于 2020 年 6 月加入富国大学。塞西莉亚获得了博士学位。在 SISSA 获得物理学博士学位，并且是加州大学圣地亚哥分校的博士后研究员，在那里她是 La Jolla Interfaces in Science 项目的一部分。她的研究重点是通过分子动力学、统计力学、粗粒度模型、实验数据和机器学习，对复杂生物物理过程进行建模的方法的开发和应用。Cecilia 的研究获得了美国国家科学基金会职业奖（2004 年）和罗伯特·A·韦尔奇基金会 Norman Hackerman 化学研究奖（2009 年）的认可。自 2016 年以来，她还担任美国国家科学基金会分子科学软件研究所的联合主任。O. Anatole von Lilienfeld 是维也纳大学物理学院计算材料发现的全职大学教授。他实验室的研究涉及使用微扰理论、机器学习和高性能计算开发基于第一原理的化合物空间理解的改进方法。此前，他是瑞士巴塞尔大学和比利时布鲁塞尔自由大学的副教授和助理教授。从 2007 年到 2013 年，在纽约大学与马克·塔克曼 (Mark Tuckerman) 和加州大学洛杉矶分校纯粹与应用数学研究所进行博士后研究后，他在阿贡和桑迪亚国家实验室工作。2005年，获博士学位。在 Ursula Rothlisberger 的指导下，获得 EPF Lausanne 的计算化学博士学位。他的毕业论文工作是在苏黎世联邦理工学院与 Martin Quack 和剑桥大学与 Nicholas Handy 一起完成的。他在苏黎世联邦理工学院、斯特拉斯堡的 Ecole de Chimie Polymers et Materiaux 和莱比锡大学学习化学。他担任IOP杂志的主编 他的毕业论文工作是在苏黎世联邦理工学院与 Martin Quack 和剑桥大学与 Nicholas Handy 一起完成的。他在苏黎世联邦理工学院、斯特拉斯堡的 Ecole de Chimie Polymers et Materiaux 和莱比锡大学学习化学。他担任IOP杂志的主编 他的毕业论文工作是在苏黎世联邦理工学院与 Martin Quack 和剑桥大学与 Nicholas Handy 一起完成的。他在苏黎世联邦理工学院、斯特拉斯堡的 Ecole de Chimie Polymers et Materiaux 和莱比锡大学学习化学。他担任IOP杂志的主编Machine Learning: Science and Technology和Science Advances编委。他一直是Nature's Scientific Data的编委从 2014 年到 2019 年，他是 2011 年开展的长期 IPAM “UCLA 项目‘导航化学化合物空间用于材料和生物设计’”的主席。他是多个奖项的获得者，包括瑞士国家科学基金会博士后资助 ( 2005)、Harry S. Truman 博士后奖学金 (2007)、Thomas Kuhn Paradigm Shift 奖 (2013)、瑞士国家科学教授奖学金 (2013)、佛兰德科学基金会的 Odysseus 资助 (2016)、ERC consolidator grant (2017) 和 Feynman纳米技术奖（2018 年）。我们要感谢评论的所有作者、确保每篇文章的清晰性和学术性的审稿人，以及《化学评论》的编辑感谢他们在准备这个系列时的支持和耐心。纪念亚历山德罗·德·维塔（Alessandro de Vita，1965-2018）。这篇文章尚未被其他出版物引用。