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Machine learning (ML) potentials, especially high-dimensional neural network potentials (HDNNPs), are crucial for atomistic simulations. This review classifies HDNNPs into four generations, detailing their evolution and applications in simulating large molecular systems.

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Area of Science:

  • Computational Chemistry
  • Materials Science
  • Physics

Background:

  • Machine learning (ML) potentials have significantly advanced atomistic simulations over the past 25 years.
  • Early neural network potentials were limited to small molecular systems.
  • The development of high-dimensional neural network potentials (HDNNPs) in 2007 enabled simulations of large systems.

Purpose of the Study:

  • To review the methodology of high-dimensional neural network potentials (HDNNPs).
  • To classify HDNNPs into four generations based on their development and capabilities.
  • To discuss the applicability, limitations, and future outlook of HDNNPs.

Main Methods:

  • Classification of HDNNPs into four generations based on methodological advancements.
  • Description of key steps in second-generation HDNNPs: energy decomposition, symmetry functions, and active learning.
  • Inclusion of long-range interactions and nonlocal phenomena in third and fourth-generation HDNNPs.

Main Results:

  • HDNNPs have evolved through four generations, expanding their capabilities for atomistic simulations.
  • Second-generation HDNNPs utilize environment-dependent energy contributions, symmetry functions, and active learning.
  • Third and fourth-generation HDNNPs incorporate long-range interactions and nonlocal phenomena like charge transfer.

Conclusions:

  • HDNNPs are a powerful and evolving tool for large-scale atomistic simulations.
  • The four-generation classification provides a framework for understanding HDNNP development.
  • Further advancements in HDNNPs promise to broaden their applicability in scientific research.