Machine Learning Force Fields
In recent years, the use of machine learning (ML) in computational chemistry has enabled numerous advances previously out of reach due to the computational complexity of traditional electronic-structure methods. One of the most promising applications is the construction of ML-based force fields (FFs...
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| Published in | Chemical reviews Vol. 121; no. 16; pp. 10142 - 10186 |
|---|---|
| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
American Chemical Society
25.08.2021
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0009-2665 1520-6890 1520-6890 |
| DOI | 10.1021/acs.chemrev.0c01111 |
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| Abstract | In recent years, the use of machine learning (ML) in computational chemistry has enabled numerous advances previously out of reach due to the computational complexity of traditional electronic-structure methods. One of the most promising applications is the construction of ML-based force fields (FFs), with the aim to narrow the gap between the accuracy of ab initio methods and the efficiency of classical FFs. The key idea is to learn the statistical relation between chemical structure and potential energy without relying on a preconceived notion of fixed chemical bonds or knowledge about the relevant interactions. Such universal ML approximations are in principle only limited by the quality and quantity of the reference data used to train them. This review gives an overview of applications of ML-FFs and the chemical insights that can be obtained from them. The core concepts underlying ML-FFs are described in detail, and a step-by-step guide for constructing and testing them from scratch is given. The text concludes with a discussion of the challenges that remain to be overcome by the next generation of ML-FFs. |
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| AbstractList | In recent years, the use of machine learning (ML) in computational chemistry has enabled numerous advances previously out of reach due to the computational complexity of traditional electronic-structure methods. One of the most promising applications is the construction of ML-based force fields (FFs), with the aim to narrow the gap between the accuracy of ab initio methods and the efficiency of classical FFs. The key idea is to learn the statistical relation between chemical structure and potential energy without relying on a preconceived notion of fixed chemical bonds or knowledge about the relevant interactions. Such universal ML approximations are in principle only limited by the quality and quantity of the reference data used to train them. This review gives an overview of applications of ML-FFs and the chemical insights that can be obtained from them. The core concepts underlying ML-FFs are described in detail, and a step-by-step guide for constructing and testing them from scratch is given. The text concludes with a discussion of the challenges that remain to be overcome by the next generation of ML-FFs.In recent years, the use of machine learning (ML) in computational chemistry has enabled numerous advances previously out of reach due to the computational complexity of traditional electronic-structure methods. One of the most promising applications is the construction of ML-based force fields (FFs), with the aim to narrow the gap between the accuracy of ab initio methods and the efficiency of classical FFs. The key idea is to learn the statistical relation between chemical structure and potential energy without relying on a preconceived notion of fixed chemical bonds or knowledge about the relevant interactions. Such universal ML approximations are in principle only limited by the quality and quantity of the reference data used to train them. This review gives an overview of applications of ML-FFs and the chemical insights that can be obtained from them. The core concepts underlying ML-FFs are described in detail, and a step-by-step guide for constructing and testing them from scratch is given. The text concludes with a discussion of the challenges that remain to be overcome by the next generation of ML-FFs. In recent years, the use of machine learning (ML) in computational chemistry has enabled numerous advances previously out of reach due to the computational complexity of traditional electronic-structure methods. One of the most promising applications is the construction of ML-based force fields (FFs), with the aim to narrow the gap between the accuracy of ab initio methods and the efficiency of classical FFs. The key idea is to learn the statistical relation between chemical structure and potential energy without relying on a preconceived notion of fixed chemical bonds or knowledge about the relevant interactions. Such universal ML approximations are in principle only limited by the quality and quantity of the reference data used to train them. This review gives an overview of applications of ML-FFs and the chemical insights that can be obtained from them. The core concepts underlying ML-FFs are described in detail, and a step-by-step guide for constructing and testing them from scratch is given. The text concludes with a discussion of the challenges that remain to be overcome by the next generation of ML-FFs. In recent years, the use of machine learning (ML) in computational chemistry has enabled numerous advances previously out of reach due to the computational complexity of traditional electronic-structure methods. One of the most promising applications is the construction of ML-based force fields (FFs), with the aim to narrow the gap between the accuracy of ab initio methods and the efficiency of classical FFs. The key idea is to learn the statistical relation between chemical structure and potential energy without relying on a preconceived notion of fixed chemical bonds or knowledge about the relevant interactions. Such universal ML approximations are in principle only limited by the quality and quantity of the reference data used to train them. This review gives an overview of applications of ML-FFs and the chemical insights that can be obtained from them. The core concepts underlying ML-FFs are described in detail, and a step-by-step guide for constructing and testing them from scratch is given. The text concludes with a discussion of the challenges that remain to be overcome by the next generation of ML-FFs. In recent years, the use of machine learning (ML) in computational chemistry has enabled numerous advances previously out of reach due to the computational complexity of traditional electronic-structure methods. One of the most promising applications is the construction of ML-based force fields (FFs), with the aim to narrow the gap between the accuracy of methods and the efficiency of classical FFs. The key idea is to learn the statistical relation between chemical structure and potential energy without relying on a preconceived notion of fixed chemical bonds or knowledge about the relevant interactions. Such universal ML approximations are in principle only limited by the quality and quantity of the reference data used to train them. This review gives an overview of applications of ML-FFs and the chemical insights that can be obtained from them. The core concepts underlying ML-FFs are described in detail, and a step-by-step guide for constructing and testing them from scratch is given. The text concludes with a discussion of the challenges that remain to be overcome by the next generation of ML-FFs. |
| Author | Sauceda, Huziel E Poltavsky, Igor Müller, Klaus-Robert Unke, Oliver T Gastegger, Michael Tkatchenko, Alexandre Chmiela, Stefan Schütt, Kristof T |
| AuthorAffiliation | Department of Artificial Intelligence Korea University Machine Learning Group Max Planck Institute for Informatics, Stuhlsatzenhausweg Department of Physics and Materials Science BASLEARN, BASF-TU Joint Lab Google Research, Brain Team BIFOLD−Berlin Institute for the Foundations of Learning and Data DFG Cluster of Excellence “Unifying Systems in Catalysis” (UniSysCat) |
| AuthorAffiliation_xml | – name: Department of Artificial Intelligence – name: Korea University – name: BIFOLD−Berlin Institute for the Foundations of Learning and Data – name: DFG Cluster of Excellence “Unifying Systems in Catalysis” (UniSysCat) – name: Department of Physics and Materials Science – name: Google Research, Brain Team – name: Machine Learning Group – name: Max Planck Institute for Informatics, Stuhlsatzenhausweg – name: BASLEARN, BASF-TU Joint Lab |
| Author_xml | – sequence: 1 givenname: Oliver T orcidid: 0000-0001-7503-406X surname: Unke fullname: Unke, Oliver T organization: DFG Cluster of Excellence “Unifying Systems in Catalysis” (UniSysCat) – sequence: 2 givenname: Stefan surname: Chmiela fullname: Chmiela, Stefan organization: Machine Learning Group – sequence: 3 givenname: Huziel E orcidid: 0000-0001-6091-3408 surname: Sauceda fullname: Sauceda, Huziel E organization: BASLEARN, BASF-TU Joint Lab – sequence: 4 givenname: Michael surname: Gastegger fullname: Gastegger, Michael organization: BASLEARN, BASF-TU Joint Lab – sequence: 5 givenname: Igor orcidid: 0000-0002-3188-7017 surname: Poltavsky fullname: Poltavsky, Igor organization: Department of Physics and Materials Science – sequence: 6 givenname: Kristof T orcidid: 0000-0001-8342-0964 surname: Schütt fullname: Schütt, Kristof T organization: Machine Learning Group – sequence: 7 givenname: Alexandre orcidid: 0000-0002-1012-4854 surname: Tkatchenko fullname: Tkatchenko, Alexandre email: alexandre.tkatchenko@uni.lu organization: Department of Physics and Materials Science – sequence: 8 givenname: Klaus-Robert orcidid: 0000-0002-3861-7685 surname: Müller fullname: Müller, Klaus-Robert email: klaus-robert.mueller@tu-berlin.de organization: Google Research, Brain Team |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33705118$$D View this record in MEDLINE/PubMed |
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| Title | Machine Learning Force Fields |
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