A new machine learning framework allows coarse-grained models to simulate complex polymer materials while adhering to thermodynamic laws. This development addresses a long-standing challenge in materials science. Researchers have struggled to simulate polymers accurately due to their complexity.
Traditional coarse-graining methods simplify atomic groups into mesoscopic particles. This approach often sacrifices physical accuracy. These models typically reproduce either equilibrium structure or large-scale dynamics, but rarely both. They often fail to capture the entropic and viscous forces crucial for polymer flow and mechanical performance.
The new artificial intelligence (AI) architecture learns coarse-grained dynamics directly from data. It is mathematically designed to prevent violations of thermodynamic laws. This ensures that the models recover missing entropic and viscous forces by construction. The framework incorporates the metriplectic bracket, a mathematical structure from non-equilibrium thermodynamics.
This structure describes how soft materials like polymers flow and deform. Translating this into a neural network skeleton creates a system that conserves energy and obeys the Second Law of Thermodynamics. The framework also uses a self-supervised learning strategy. This allows the network to discover hidden variables like entropy and internal microstructure from particle movement. This makes it possible to train predictive models from experimental trajectories.
The researchers tested the method on star polymers and dense colloidal suspensions. It accurately recovered both radial structure and non-equilibrium dynamics for star polymers. It also learned a model from high-speed video for colloidal suspensions. This model captured localized rearrangement events that drive emergent flow. This links microstructure to macroscopic rheology.
The team released open-source implementations in PyTorch and LAMMPS. This allows for training and large-scale inference. The LAMMPS implementation works with millions of coarse-grained particles. This offers a path to thermodynamically consistent, data-driven models of polymeric materials at an engineering scale.
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