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Molecular Denoising Using Diffusion Models with Physics-Informed Priors.

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Physics-informed priors accelerate Denoising Diffusion Probabilistic Models (DDPMs) for atomistic systems. This method leverages statistical mechanics to improve training efficiency and speed up the generation of high-quality molecular samples.

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Area of Science:

  • Computational material science
  • Machine learning for physics
  • Statistical mechanics

Background:

  • Denoising Diffusion Probabilistic Models (DDPMs) excel in generative tasks for material science and molecular modeling.
  • DDPMs require many iterations for high-quality sample generation, leading to slow sampling speeds.

Purpose of the Study:

  • To enhance DDPMs for atomistic systems by incorporating physics-informed priors.
  • To accelerate the sampling process and improve training efficiency of DDPMs.

Main Methods:

  • Leveraging thermodynamics and statistical mechanics to derive physics-informed parameters for prior distributions.
  • Initializing the Markov chain closer to the true data distribution using these derived parameters.
  • Applying the method to denoise radial distribution functions from atomic configurations of Lennard-Jones and multiatomic liquids.

Main Results:

  • The proposed strategy significantly shortens the Markov chain length.
  • Improved training efficiency and accelerated sampling process for DDPMs.
  • Effective denoising of radial distribution functions for various liquid systems was demonstrated.

Conclusions:

  • Physics-informed priors derived from statistical mechanics offer a powerful strategy to accelerate DDPMs in atomistic simulations.
  • This approach enhances the practical applicability of DDPMs in material science and molecular modeling by addressing sampling speed limitations.