Applications and training sets of machine learning potentials
Recently, machine learning potentials (MLPs) have been attracting interest as an alternative to the computationally expensive density-functional theory (DFT) calculations. The data-driven approach in MLPs requires carefully curated training datasets, which define the valid domain of simulations. The...
Main Authors: | , , , , , , |
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Format: | Article |
Language: | English |
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Taylor & Francis Group
2023-10-01
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Series: | Science and Technology of Advanced Materials: Methods |
Subjects: | |
Online Access: | http://dx.doi.org/10.1080/27660400.2023.2269948 |
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author | Changho Hong Jaehoon Kim Jaesun Kim Jisu Jung Suyeon Ju Jeong Min Choi Seungwu Han |
author_facet | Changho Hong Jaehoon Kim Jaesun Kim Jisu Jung Suyeon Ju Jeong Min Choi Seungwu Han |
author_sort | Changho Hong |
collection | DOAJ |
description | Recently, machine learning potentials (MLPs) have been attracting interest as an alternative to the computationally expensive density-functional theory (DFT) calculations. The data-driven approach in MLPs requires carefully curated training datasets, which define the valid domain of simulations. Therefore, acquiring training datasets that comprehensively span the domain of the desired simulations is important. In this review, we attempt to set guidelines for the systematic construction of training datasets according to target simulations. To this end, we extensively analyze the training sets in previous literature according to four application types: thermal properties, diffusion properties, structure prediction, and chemical reactions. In each application, we summarize characteristic reference structures and discuss specific parameters for DFT calculations such as MD conditions. We hope this review serves as a comprehensive guide for researchers and practitioners aiming to harness the capabilities of MLPs in material simulations. |
first_indexed | 2024-03-11T13:39:55Z |
format | Article |
id | doaj.art-f5ca5df4b3014789848f16b9b0972d47 |
institution | Directory Open Access Journal |
issn | 2766-0400 |
language | English |
last_indexed | 2024-03-11T13:39:55Z |
publishDate | 2023-10-01 |
publisher | Taylor & Francis Group |
record_format | Article |
series | Science and Technology of Advanced Materials: Methods |
spelling | doaj.art-f5ca5df4b3014789848f16b9b0972d472023-11-02T13:48:31ZengTaylor & Francis GroupScience and Technology of Advanced Materials: Methods2766-04002023-10-010010.1080/27660400.2023.22699482269948Applications and training sets of machine learning potentialsChangho Hong0Jaehoon Kim1Jaesun Kim2Jisu Jung3Suyeon Ju4Jeong Min Choi5Seungwu Han6Seoul National UniversitySeoul National UniversitySeoul National UniversitySeoul National UniversitySeoul National UniversitySeoul National UniversitySeoul National UniversityRecently, machine learning potentials (MLPs) have been attracting interest as an alternative to the computationally expensive density-functional theory (DFT) calculations. The data-driven approach in MLPs requires carefully curated training datasets, which define the valid domain of simulations. Therefore, acquiring training datasets that comprehensively span the domain of the desired simulations is important. In this review, we attempt to set guidelines for the systematic construction of training datasets according to target simulations. To this end, we extensively analyze the training sets in previous literature according to four application types: thermal properties, diffusion properties, structure prediction, and chemical reactions. In each application, we summarize characteristic reference structures and discuss specific parameters for DFT calculations such as MD conditions. We hope this review serves as a comprehensive guide for researchers and practitioners aiming to harness the capabilities of MLPs in material simulations.http://dx.doi.org/10.1080/27660400.2023.2269948machine learning potentialtraining setpotential energy surfacedensity functional theorymolecular dynamics |
spellingShingle | Changho Hong Jaehoon Kim Jaesun Kim Jisu Jung Suyeon Ju Jeong Min Choi Seungwu Han Applications and training sets of machine learning potentials Science and Technology of Advanced Materials: Methods machine learning potential training set potential energy surface density functional theory molecular dynamics |
title | Applications and training sets of machine learning potentials |
title_full | Applications and training sets of machine learning potentials |
title_fullStr | Applications and training sets of machine learning potentials |
title_full_unstemmed | Applications and training sets of machine learning potentials |
title_short | Applications and training sets of machine learning potentials |
title_sort | applications and training sets of machine learning potentials |
topic | machine learning potential training set potential energy surface density functional theory molecular dynamics |
url | http://dx.doi.org/10.1080/27660400.2023.2269948 |
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