arXiv Machine Learning
Sep 17

Machine learning kinetics from molecular dynamics data

The article reviews modern machine learning techniques for estimating the committor and related kinetic statistics from molecular dynamics simulations. It emphasizes self‑supervised methods that solve the underlying dynamical equations instead of relying on labeled data, and unifies various approaches—generator‑based PDEs, variational principles, Markov state models, dynamical Galerkin approximation, and neural networks—under a common operator framework. The review also discusses practical guidance for handling non‑Markovian effects, sampling strategies, and outlines future research directions such as connections to reinforcement learning and generative modeling.

By Jonathan Weare, Aaron R. Dinner
arXiv Machine Learning
Aug 27

Data-driven Effective Modeling of Stochastic Chemical Reaction Networks

The paper introduces a data-driven effective model for stochastic chemical reaction networks that bypasses the high computational cost of the Stochastic Simulation Algorithm (SSA). By approximating the finite-time transition kernel of the SSA-induced continuous-time Markov chain with a generative machine learning model, the method operates on a user-defined coarse time step independent of microscopic reaction events. Using a conditional normalizing flow as the stochastic propagator, the trained model recursively generates statistically consistent trajectories, achieving significant computational savings while maintaining accuracy, as demonstrated through numerous numerical examples.

By Yuan Chen, Weize Mao, Dongbin Xiu
arXiv Machine Learning
Jun 10

Rare Event Analysis via Stochastic Optimal Control

arXiv:2604. 13213v2 Announce Type: replace-cross Abstract: Rare events such as conformational changes in biomolecules, phase transitions, and chemical reactions are central to the behavior of many physical systems, yet they are extremely difficult to study computationally because unbiased simulations seldom produce them.

By Yuanqi Du, Jiajun He, Dinghuai Zhang, Eric Vanden-Eijnden, Carles Domingo-Enrich
arXiv Machine Learning
Sep 15

Physics-Constrained Neural Surrogate for Domain Growth Prediction in Systems with Conserved Kinetics

The paper introduces a physics-constrained neural network surrogate that learns the microstructural evolution of binary mixtures governed by the Cahn‑Hilliard equation. By imposing conservation of the order parameter as a hard constraint on the network output, the model accurately predicts long‑time phase‑separation dynamics for both critical and off‑critical mixtures, maintaining mixture composition and matching the Lifshitz‑Slyozov domain‑growth law. A variant that enforces conservation only through a penalty term drifts from the initial composition and loses predictive accuracy over long rollouts, underscoring the necessity of the hard constraint for stability.

By Vijay Yadav, Pallvi Pandey, Madhu Priya, Manish Dev Shrimali, Prabhat K. Jaiswal