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Automatic Feature Selection for Atom-Centered Neural Network Potentials Using a Gradient Boosting Decision Algorithm.

Renzhe Li1, Jiaqi Wang1, Akksay Singh1,2,3

  • 1Department of Materials Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, People's Republic of China.

Journal of Chemical Theory and Computation
|November 19, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an automated method using gradient boosting decision trees (GBDT) to select optimal atom-centered symmetry functions (ACSFs) for accurate atom-centered neural network (ANN) potentials. The approach enhances machine learning potential development by improving accuracy and efficiency.

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Area of Science:

  • Computational materials science
  • Machine learning in chemistry and physics
  • Method development for materials modeling

Background:

  • Atom-centered neural network (ANN) potentials offer high accuracy and efficiency for atomic system simulations.
  • The performance of ANN potentials critically depends on the appropriate selection of atom-centered symmetry functions (ACSFs) that describe atomic environments.
  • Suboptimal ACSF selection can significantly degrade the quality and predictive power of ANN potentials.

Purpose of the Study:

  • To develop an automated framework for selecting optimal atom-centered symmetry functions (ACSFs) for atom-centered neural network (ANN) potentials.
  • To improve the accuracy and computational efficiency of ANN potentials through systematic feature selection.
  • To provide a robust method for identifying the most relevant atomic features for machine learning potential development.

Main Methods:

  • Implementation of a gradient boosting decision tree (GBDT) based framework for automatic ACSF selection.
  • Evaluation of the relative importance of a comprehensive set of uniformly distributed ACSFs.
  • Training and validation of ANN potentials using the selected optimal ACSFs, benchmarked against grid searching and other feature selection algorithms.

Main Results:

  • The GBDT-based framework successfully identified a minimal set of 18 ACSFs for the Germanium (Ge) system.
  • Achieved high accuracy with root-mean-square errors (RMSE) of 10.2 meV/atom for energy and 84.8 meV/Å for force predictions.
  • Demonstrated superior performance compared to commonly used feature selection algorithms, validating the selected ACSFs' optimality.

Conclusions:

  • The proposed GBDT framework provides an effective and accurate method for automatic ACSF selection in ANN potential development.
  • Optimized ACSF selection significantly enhances the balance between accuracy and computational efficiency of machine learning potentials.
  • This approach facilitates the development of more reliable and performant machine learning potentials for materials simulations.