New Framework Ensures Thermodynamic Consistency in Machine‑Learned Coarse‑Grained Models

Global: New Framework Ensures Thermodynamic Consistency in Machine‑Learned Coarse‑Grained Models

Researchers from an international team have introduced a machine‑learning framework that constructs coarse‑grained particle models while rigorously preserving the first and second laws of thermodynamics, momentum conservation, and fluctuation‑dissipation balance. The work, posted on arXiv in August 2025, addresses long‑standing difficulties in simulating multiscale systems where fine‑grained dynamics must be linked to emergent bulk behavior.

Background

Multiscale phenomena appear across physics, chemistry, and engineering, yet traditional coarse‑graining often discards information, leading to dissipative and history‑dependent dynamics that are difficult to reproduce accurately. Existing data‑driven approaches can capture some statistical features but typically lack guarantees of thermodynamic consistency.

Metriplectic Approach

The authors adopt the metriplectic bracket formalism, a mathematical structure that combines Hamiltonian (energy‑conserving) and dissipative components. By discretizing this bracket, the framework enforces discrete analogues of energy conservation, entropy production, and momentum balance, ensuring that learned models obey fundamental physical principles by construction.

Self‑Supervised Variable Discovery

Because entropic state variables are not directly observable, the team proposes a self‑supervised learning scheme that infers emergent structural variables from trajectory data alone. The method iteratively refines latent representations to align with the metriplectic constraints, eliminating the need for manually labeled thermodynamic quantities.

Benchmark Validation

Tests on standard benchmark systems demonstrate that the approach reproduces equilibrium distributions and non‑equilibrium response functions more accurately than baseline neural‑network models. In a particularly challenging case, the framework coarse‑grains star‑polymer chains at high reduction levels while retaining correct non‑equilibrium statistics.

Real‑World Demonstrations

The authors further apply the technique to high‑speed video recordings of colloidal suspensions. The learned models capture the coupling between localized particle rearrangements and the resulting stochastic macroscopic dynamics, offering insights into material behavior under shear and other external forces.

Open‑Source Tools and Outlook

Implementations in both PyTorch and LAMMPS are released under an open‑source license, enabling large‑scale inference and adaptation to a wide range of particle‑based systems. The authors anticipate that the framework will facilitate more reliable simulations in soft‑matter physics, materials science, and related fields.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.