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Parallel Multistream Training of High-Dimensional Neural Network Potentials.

Andreas Singraber1, Tobias Morawietz2, Jörg Behler3

  • 1Faculty of Physics , University of Vienna , Boltzmanngasse 5 , Vienna , Austria.

Journal of Chemical Theory and Computation
|April 18, 2019
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Summary
This summary is machine-generated.

We present an efficient method for training high-dimensional neural network potentials (HDNNPs) using Kalman filtering. This approach optimizes HDNNP training, improving accuracy for chemical and materials simulations.

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Area of Science:

  • Computational Chemistry
  • Materials Science
  • Physics

Background:

  • High-dimensional neural network potentials (HDNNPs) accurately reproduce ab initio potential energy surfaces, becoming vital tools in various scientific fields.
  • The training of neural networks is central to the HDNNP method's effectiveness.

Purpose of the Study:

  • To develop and present an efficient approach for optimizing the weight parameters of neural networks used in HDNNPs.
  • To determine optimal settings for Kalman filter parameters and enhance HDNNP training results.

Main Methods:

  • Utilizing multistream Kalman filtering for optimizing neural network weight parameters.
  • Employing potential energies and forces as reference data for training.
  • Conducting a large parameter study to identify optimal Kalman filter settings.

Main Results:

  • Demonstrated an efficient approach for optimizing HDNNP training.
  • Identified optimal settings for Kalman filter parameters, significantly impacting fit quality.
  • Successfully applied the training approach to water and developed a new potential for copper sulfide (Cu2S).

Conclusions:

  • The presented Kalman filtering approach effectively optimizes HDNNP training.
  • The new Cu2S potential accurately reproduces its complex solid structure and superionic phase transition behavior in molecular dynamics simulations.