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Suman Bhaumik1, Dayou Zhang1, Yinan Shu1

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This study introduces a dual-level neural network (NN) to improve machine-learned potential energy surfaces. The method enhances accuracy in data-scarce regions, ensuring reliable chemical dynamics simulations.

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Area of Science:

  • Computational Chemistry
  • Machine Learning in Quantum Mechanics
  • Method Development

Background:

  • Machine-learned potential energy surfaces (PES) often lack reliability in data-sparse regions.
  • Accurate PES are crucial for molecular dynamics simulations and understanding chemical reactions.
  • Existing methods struggle with extrapolation and require extensive training data.

Purpose of the Study:

  • To develop a cost-effective method for improving the accuracy of machine-learned PES in critical regions.
  • To incorporate known physical constraints (asymptotic behavior, short-range repulsion) into data-driven models.
  • To achieve high accuracy for chemical dynamics without prohibitive computational cost.

Main Methods:

  • Introduction of a dual-level parametrically managed neural network (DL-PMNN).
  • Utilizes two levels of electronic structure calculations: a high-level (HL) accurate method and a lower-level (LL) inexpensive method.
  • Employs a neural network with a parametrically managed activation function (PMAF).

Main Results:

  • The DL-PMNN successfully fits the potential energy surface for S-H bond dissociation in ortho-fluorothiophenol.
  • The method ensures correct PES behavior at both large and small interatomic distances.
  • Achieved high accuracy comparable to HL calculations for dynamics simulations.

Conclusions:

  • The DL-PMNN offers an efficient and accurate approach for constructing reliable potential energy surfaces.
  • This method addresses the limitations of traditional machine learning models in data-scarce environments.
  • Enables accurate and crash-free molecular dynamics simulations using data-driven potentials.