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Updated: Dec 29, 2025

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Machine learning for interatomic potential models.

Tim Mueller1, Alberto Hernandez1, Chuhong Wang1

  • 1Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA.

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|February 10, 2020
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Summary
This summary is machine-generated.

Supervised machine learning is accelerating molecular and materials research by creating accurate interatomic potential models for atomic-scale simulations. This perspective discusses recent advancements and emerging methods like moment tensor potentials and message-passing networks.

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Area of Science:

  • Computational Materials Science
  • Machine Learning in Chemistry
  • Materials Informatics

Background:

  • Supervised machine learning significantly accelerates atomic-scale simulations.
  • Interatomic potential models are crucial for molecular and materials research.
  • Previous reviews have not extensively covered emerging machine learning approaches.

Purpose of the Study:

  • To provide an updated discussion on machine-learned interatomic potentials.
  • To highlight recent developments and emerging trends in the field.
  • To introduce three novel approaches: moment tensor potentials, message-passing networks, and symbolic regression.

Main Methods:

  • Review of recent literature on machine-learned interatomic potentials.
  • Discussion of emerging methods including moment tensor potentials, message-passing networks, and symbolic regression.
  • Analysis of advancements in supervised machine learning for potential development.

Main Results:

  • Machine learning significantly enhances the speed and accuracy of interatomic potential models.
  • Emerging methods offer new avenues for developing sophisticated potential models.
  • The field is rapidly evolving with promising future research directions.

Conclusions:

  • Machine-learned interatomic potentials are transforming molecular and materials research.
  • Continued development of these models promises further acceleration of atomic-scale simulations.
  • Novel approaches like moment tensor potentials and message-passing networks show significant potential.