[en] The rapid development and large body of literature on machine learning interatomic potentials (MLIPs) can make it difficult to know how to proceed for researchers who are not experts but wish to use these tools. The spirit of this review is to help such researchers by serving as a practical, accessible guide to the state-of-the-art in MLIPs. This review paper covers a broad range of topics related to MLIPs, including (i) central aspects of how and why MLIPs are enablers of many exciting advancements in molecular modeling, (ii) the main underpinnings of different types of MLIPs, including their basic structure and formalism, (iii) the potentially transformative impact of universal MLIPs for both organic and inorganic systems, including an overview of the most recent advances, capabilities, downsides, and potential applications of this nascent class of MLIPs, (iv) a practical guide for estimating and understanding the execution speed of MLIPs, including guidance for users based on hardware availability, type of MLIP used, and prospective simulation size and time, (v) a manual for what MLIP a user should choose for a given application by considering hardware resources, speed requirements, energy and force accuracy requirements, as well as guidance for choosing pre-trained potentials or fitting a new potential from scratch, (vi) discussion around MLIP infrastructure, including sources of training data, pre-trained potentials, and hardware resources for training, (vii) summary of some key limitations of present MLIPs and current approaches to mitigate such limitations, including methods of including long-range interactions, handling magnetic systems, and treatment of excited states, and finally (viii) we finish with some more speculative thoughts on what the future holds for the development and application of MLIPs over the next 3–10+ years.
Disciplines :
Chemistry
Author, co-author :
Jacobs, Ryan; Department of Materials Science and Engineering, University of Wisconsin-Madison, Madison, United States
Morgan, Dane; Department of Materials Science and Engineering, University of Wisconsin-Madison, Madison, United States
Attarian, Siamak; Department of Materials Science and Engineering, University of Wisconsin-Madison, Madison, United States
Meng, Jun; Department of Materials Science and Engineering, University of Wisconsin-Madison, Madison, United States
Shen, Chen; Department of Materials Science and Engineering, University of Wisconsin-Madison, Madison, United States
Wu, Zhenghao; Department of Chemistry and Materials Science, Xi'an Jiaotong-Liverpool University, Suzhou, China
Xie, Clare Yijia; John A, Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, United States
Yang, Julia H.; Harvard University Center for the Environment, Harvard University, Cambridge, United States ; John A, Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, United States
Artrith, Nongnuch; Materials Chemistry and Catalysis, Debye Institute for Nanomaterials Science, Utrecht University, Utrecht, Netherlands
Blaiszik, Ben; Globus, University of Chicago, Chicago, United States ; Data Science and Learning Division, Argonne National Laboratory, Lemont, United States
Ceder, Gerbrand; Department of Materials Science and Engineering, University of California, Berkeley, United States ; Materials Sciences Division, Lawrence Berkeley National Laboratory, United States
Choudhary, Kamal; Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, United States
Csanyi, Gabor; Department of Engineering, University of Cambridge, Cambridge, United Kingdom
Cubuk, Ekin Dogus; Google DeepMind, Mountain View, United States
Deng, Bowen; Department of Materials Science and Engineering, University of California, Berkeley, United States ; Materials Sciences Division, Lawrence Berkeley National Laboratory, United States
Drautz, Ralf; Interdisciplinary Centre for Advanced Materials Simulation (ICAMS), Ruhr-Universität Bochum, Bochum, Germany
Fu, Xiang; Fundamental AI Research (FAIR) at Meta, United States
Godwin, Jonathan; Orbital Materials, London, United Kingdom
Honavar, Vasant; Department of Computer Science and Engineering, The Pennsylvania State University, University Park, United States ; College of Information Sciences and Technology, The Pennsylvania State University, University Park, United States ; Artificial Intelligence Research Laboratory, The Pennsylvania State University, United States ; Center for Artificial Intelligence Foundations and Scientific Applications, The Pennsylvania State University, United States
Isayev, Olexandr; Department of Chemistry, Mellon College of Science, Carnegie Mellon University, United States ; Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, United States
Johansson, Anders; John A, Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, United States
Martiniani, Stefano; Courant Institute of Mathematical Sciences, New York University, New York, United States ; Center for Soft Matter Research, Department of Physics, New York University, New York, United States ; Simons Center for Computational Physical Chemistry, Department of Chemistry, New York University, New York, United States
Ong, Shyue Ping; Aiiso Yufeng Li Family Department of Chemical and Nano Engineering, University of California, San Diego, La Jolla, United States
POLTAVSKYI, Igor ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Physics and Materials Science (DPHYMS)
Schmidt, K.J.; Globus, University of Chicago, Chicago, United States ; Data Science and Learning Division, Argonne National Laboratory, Lemont, United States
Takamoto, So; Preferred Networks, Inc., Chiyoda-ku, Japan
Thompson, Aidan P.; Center for Computing Research, Sandia National Laboratories, Albuquerque, Mexico
Westermayr, Julia; Wilhelm-Ostwald-Institut für Physikalische und Theoretische Chemie, Universität Leipzig, Germany
Wood, Brandon M.; Fundamental AI Research (FAIR) at Meta, United States
Kozinsky, Boris; John A, Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, United States
KC thanks the National Institute of Standards and Technology for funding, computational, and data management resources. This work was performed with funding from the CHIPS Metrology Program, part of CHIPS for America, National Institute of Standards and Technology, U.S. Department of Commerce. Certain commercial equipment, instruments, software, or materials are identified in this paper in order to specify the experimental procedure adequately. Such identifications are not intended to imply recommendation or endorsement by NIST, nor it is intended to imply that the materials or equipment identified are necessarily the best available for the purpose.Funding for the \u201CMachine Learning Potentials \u2013 Status and Future (MLIP-SAFE)\u201D workshop and development of this paper was provided by the National Science Foundation through an AI Institute Planning Grant, Award Number 2020243. KC thanks the National Institute of Standards and Technology for funding, computational, and data management resources. This work was performed with funding from the CHIPS Metrology Program, part of CHIPS for America, National Institute of Standards and Technology, U.S. Department of Commerce. Certain commercial equipment, instruments, software, or materials are identified in this paper in order to specify the experimental procedure adequately. Such identifications are not intended to imply recommendation or endorsement by NIST, nor it is intended to imply that the materials or equipment identified are necessarily the best available for the purpose. SM acknowledges support from NSF Grant OAC-2311632 and the Simons Center for Computational Physical Chemistry (Simons Foundation grant 839534, MT).Funding for the \u201CMachine Learning Potentials \u2013 Status and Future (MLP-SAFE)\u201D workshop and development of this paper was provided by the National Science Foundation through an AI Institute Planning Grant, Award Number 2020243.
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