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Learning Force Field Parameters from Differentiable Particle-Field Molecular Dynamics.

Manuel Carrer1, Henrique Musseli Cezar1, Sigbjørn Løland Bore1

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|July 4, 2024
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Summary
This summary is machine-generated.

We introduce ∂-HylleraasMD (∂-HyMD), a differentiable molecular dynamics software for automated force field optimization. This method improves accuracy and parameter transferability for lipid and triglyceride simulations.

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Area of Science:

  • Computational chemistry
  • Molecular dynamics
  • Software development

Background:

  • Accurate molecular dynamics simulations rely on precise force field parameters.
  • Optimizing these parameters traditionally involves computationally intensive and often manual processes.
  • Existing differentiable molecular dynamics methods face challenges with memory and numerical stability.

Purpose of the Study:

  • To develop a fully end-to-end differentiable molecular dynamics software, ∂-HylleraasMD (∂-HyMD).
  • To establish an automated protocol for optimizing molecular force field parameters.
  • To address limitations of existing differentiable molecular dynamics approaches.

Main Methods:

  • Developed ∂-HylleraasMD (∂-HyMD) using the JAX autodiff framework and Hamiltonian hybrid particle-field formalism.
  • Implemented an embarrassingly parallel optimization algorithm for simultaneous simulation and gradient calculation.
  • Utilized reverse mode automatic differentiation for iterative force field parameter optimization.

Main Results:

  • Achieved faster convergence in force field parameter minimization compared to gradient-free methods.
  • Generated a force field library for phospholipids with improved density profiles versus all-atom references.
  • Demonstrated accurate prediction of properties not in the learning set, like lateral pressure profiles.

Conclusions:

  • ∂-HyMD offers an efficient and stable approach for automated force field parameter optimization.
  • The developed force fields exhibit enhanced accuracy and transferability to related systems like triglycerides.
  • This work advances the capability of differentiable molecular dynamics in computational chemistry.