Roadmap for the development of machine learning-based interatomic potentials

Abstract An interatomic potential, traditionally regarded as a mathematical function, serves to depict atomic interactions within molecules or solids by expressing potential energy concerning atom positions. These potentials are pivotal in materials science and engineering, facilitating atomic-scale simulations, predictive material behavior, accelerated discovery, and property optimization. Notably, the landscape is evolving with machine learning transcending conventional mathematical models. Various machine learning-based interatomic potentials, such as artificial neural networks, kernel-based methods, deep learning, and physics-informed models, have emerged, each wielding unique strengths and limitations. These methods decode the intricate connection between atomic configurations and potential energies, offering advantages like precision, adaptability, insights, and seamless integration. The transformative potential of machine learning-based interatomic potentials looms large in materials science and engineering. They promise tailor-made materials discovery and optimized properties for specific applications. Yet, formidable challenges persist, encompassing data quality, computational demands, transferability, interpretability, and robustness. Tackling these hurdles is imperative for nurturing accurate, efficient, and dependable machine learning-based interatomic potentials primed for widespread adoption in materials science and engineering. This roadmap offers an appraisal of the current machine learning-based interatomic potential landscape, delineates the associated challenges, and envisages how progress in this domain can empower atomic-scale modeling of the composition-processing-microstructure-property relationship, underscoring its significance in materials science and engineering.

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Publication Details

Journal
Modelling and Simulation in Materials Science and Engineering
Published
2024-12-11
DOI
https://doi.org/10.1088/1361-651x/ad9d63
Citations
38
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
Field-Weighted Citation Impact
13.02
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article

Roadmap for the development of machine learning-based interatomic potentials

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38 citations
Modelling and Simulation in Materials Science and Engineering
EEG and Brain-Computer Interfaces
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article

Roadmap for the development of machine learning-based interatomic potentials

Shyue Ping Ong, Yutack Park, Alexander V. Shapeev, Seungwu Han, Evgeny V. Podryabinkin, Jörg Behler, Seungwoo Hwang, Rodrigo Freitas, Jaesun Kim, Zachary H. Aitken, Yuri M. Mishin, V. Sorkin, Tsz Wai Ko, Antonio Politano, Zongrui Pei, Killian Sheriff, Ju Li, Yong‐Wei Zhang, Yifan Cao, Xianglin Liu, Aidan P. Thompson, Dmitry Korogod, Olga Chalykh
article en
38 citations

Abstract

Abstract An interatomic potential, traditionally regarded as a mathematical function, serves to depict atomic interactions within molecules or solids by expressing potential energy concerning atom positions. These potentials are pivotal in materials science and engineering, facilitating atomic-scale simulations, predictive material behavior, accelerated discovery, and property optimization. Notably, the landscape is evolving with machine learning transcending conventional mathematical models. Various machine learning-based interatomic potentials, such as artificial neural networks, kernel-based methods, deep learning, and physics-informed models, have emerged, each wielding unique strengths and limitations. These methods decode the intricate connection between atomic configurations and potential energies, offering advantages like precision, adaptability, insights, and seamless integration. The transformative potential of machine learning-based interatomic potentials looms large in materials science and engineering. They promise tailor-made materials discovery and optimized properties for specific applications. Yet, formidable challenges persist, encompassing data quality, computational demands, transferability, interpretability, and robustness. Tackling these hurdles is imperative for nurturing accurate, efficient, and dependable machine learning-based interatomic potentials primed for widespread adoption in materials science and engineering. This roadmap offers an appraisal of the current machine learning-based interatomic potential landscape, delineates the associated challenges, and envisages how progress in this domain can empower atomic-scale modeling of the composition-processing-microstructure-property relationship, underscoring its significance in materials science and engineering.

Modelling and Simulation in Materials Science and EngineeringVol. 33(2)
Agency for Science, Technology and Research (SG), Korea Institute for Advanced Study (KR), Skolkovo Institute of Science and Technology (RU), Seoul National University (KR), Moscow Institute of Physics and Technology (RU), George Mason University (US), University of L'Aquila (IT), Institute of High Performance Computing (SG), University of California San Diego (US), Sandia National Laboratories (US), Peng Cheng Laboratory (CN), New York University (US), Massachusetts Institute of Technology (US), Ruhr University Bochum (DE)
Openalex Percentile: Top 1%
EEG and Brain-Computer Interfaces
13.02
DOIOpen Access PDFOpenAlex
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