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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| Vydáno v: | Chemical reviews Ročník 121; číslo 16; s. 10142 |
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| Hlavní autoři: | , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
United States
25.08.2021
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| ISSN: | 1520-6890, 1520-6890 |
| On-line přístup: | Zjistit podrobnosti o přístupu |
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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
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
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. |
| Author | Sauceda, Huziel E Poltavsky, Igor Unke, Oliver T Gastegger, Michael Müller, Klaus-Robert Schütt, Kristof T Tkatchenko, Alexandre Chmiela, Stefan |
| 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), Technische Universität Berlin, 10623 Berlin, Germany – sequence: 2 givenname: Stefan surname: Chmiela fullname: Chmiela, Stefan organization: Machine Learning Group, Technische Universität Berlin, 10587 Berlin, Germany – sequence: 3 givenname: Huziel E orcidid: 0000-0001-6091-3408 surname: Sauceda fullname: Sauceda, Huziel E organization: BASLEARN, BASF-TU Joint Lab, Technische Universität Berlin, 10587 Berlin, Germany – sequence: 4 givenname: Michael surname: Gastegger fullname: Gastegger, Michael organization: BASLEARN, BASF-TU Joint Lab, Technische Universität Berlin, 10587 Berlin, Germany – sequence: 5 givenname: Igor orcidid: 0000-0002-3188-7017 surname: Poltavsky fullname: Poltavsky, Igor organization: Department of Physics and Materials Science, University of Luxembourg, L-1511 Luxembourg City, Luxembourg – sequence: 6 givenname: Kristof T orcidid: 0000-0001-8342-0964 surname: Schütt fullname: Schütt, Kristof T organization: Machine Learning Group, Technische Universität Berlin, 10587 Berlin, Germany – sequence: 7 givenname: Alexandre orcidid: 0000-0002-1012-4854 surname: Tkatchenko fullname: Tkatchenko, Alexandre organization: Department of Physics and Materials Science, University of Luxembourg, L-1511 Luxembourg City, Luxembourg – sequence: 8 givenname: Klaus-Robert orcidid: 0000-0002-3861-7685 surname: Müller fullname: Müller, Klaus-Robert organization: Google Research, Brain Team, Berlin, Germany |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33705118$$D View this record in MEDLINE/PubMed |
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