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An entropy-maximization approach to automated training set generation for interatomic potentials.

Mariia Karabin1, Danny Perez2

  • 1Department of Chemistry, Clemson University, Clemson, South Carolina 29634, USA.

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Summary

Machine learning interatomic potentials require diverse training data. An automated sampling method maximizes local entropy, creating more diverse and cost-effective training sets than traditional approaches.

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Area of Science:

  • Computational materials science
  • Machine learning in physics
  • Atomistic simulations

Background:

  • Machine learning interatomic potentials (MLIPs) aim for high accuracy at low computational cost.
  • MLIP transferability relies heavily on training set quality and diversity.
  • Current training set generation is often labor-intensive and lacks systematic optimization.

Purpose of the Study:

  • To develop an automated method for generating high-quality training sets for MLIPs.
  • To formalize requirements for optimal training sets using a local entropy-maximization framework.
  • To improve the diversity and efficiency of MLIP training data generation.

Main Methods:

  • Formulation of a local entropy-maximization objective function.
  • Development of an automated sampling scheme based on this objective.
  • Comparison of the proposed sampling scheme against unbiased sampling and hand-crafted sets.

Main Results:

  • The proposed automated sampling scheme generates significantly more diverse training sets.
  • The method effectively avoids redundant configurations, reducing computational cost.
  • The generated training sets are competitive with meticulously hand-crafted datasets.

Conclusions:

  • Automated entropy-driven sampling is a powerful strategy for creating efficient MLIP training sets.
  • This approach enhances the transferability and reduces the cost of developing MLIPs.
  • The framework offers a systematic way to address the challenge of training data generation for MLIPs.