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Machine Learning Interatomic Force Fields for Carbon Allotropic Materials

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Artificial Intelligence for Materials Science

Part of the book series: Springer Series in Materials Science ((SSMATERIALS,volume 312))

Abstract

Recently, the machine learning (ML) atomic force field has emerged as a powerful atomic simulation approach because of its high accuracy, low computational cost, and transferability. In this mini review, we first summarize the disadvantages of traditional force field and the unique advantages of ML-based force field for molecular dynamics simulations. Then the basic workflow to develop the ML atomic force field is discussed in each step. Furthermore, taking carbon material as a typical example, the various applications of ML-based force fields for studying the carbon allotropic materials are reviewed. Finally, the perspectives are discussed and future directions for studying atomic force field by ML are given.

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Author information

Authors and Affiliations

  1. School of Mechanical Engineering, Donghua University, Shanghai, China

    Xiangjun Liu, Quanjie Wang & Jie Zhang

Authors
  1. Xiangjun Liu
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  2. Quanjie Wang
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  3. Jie Zhang
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Corresponding author

Correspondence to Xiangjun Liu .

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Editors and Affiliations

  1. Monash Suzhou Research Institute, Suzhou, China

    Yuan Cheng

  2. Institute of Artificial Intelligence, Beihang University, Beijing, China

    Tian Wang

  3. Institute of High Performance Computing, Singapore, Singapore

    Gang Zhang

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Liu, X., Wang, Q., Zhang, J. (2021). Machine Learning Interatomic Force Fields for Carbon Allotropic Materials. In: Cheng, Y., Wang, T., Zhang, G. (eds) Artificial Intelligence for Materials Science. Springer Series in Materials Science, vol 312. Springer, Cham. https://doi.org/10.1007/978-3-030-68310-8_4

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