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Area of Science:

  • Computational materials science
  • Molecular dynamics simulations
  • Machine learning in chemistry

Background:

  • Deep neural networks are popular for coarse-grained molecular dynamics (CGMD) potentials due to their complexity and training ease.
  • Traditional functional forms are simpler but may lack descriptive power for complex systems.

Purpose of the Study:

  • To investigate the potential advantages of data-driven optimization of simpler functional forms for CGMD.
  • To develop and evaluate a genetic algorithm for optimizing Lennard-Jones potentials.

Main Methods:

  • A genetic algorithm was developed to optimize Lennard-Jones potentials for CGMD models.
  • Optimization was based on both structural and thermodynamic data for crystal and liquid crystal materials.
  • Detailed descriptions of the algorithm, loss function, and hyperparameters are provided.

Main Results:

  • Optimized models reproduced a wider range of physical properties than simpler parametrization schemes.
  • Models demonstrated surprising transferability, predicting properties not included in training.
  • Simulations showed stabilization of crystal structures, preserved melting-point trends, and reproduced liquid crystalline phase transitions.

Conclusions:

  • Simpler functional forms, when coupled with data-driven training algorithms like genetic algorithms, retain significant untapped potential for CGMD.
  • This approach offers a viable and effective alternative to complex neural network potentials for certain CGMD applications.