arXiv Machine Learning

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.

arXiv Machine Learning

Sep 18

Correlation-Free Transition Path Sampling through Shooting Point Generation Guided by Committor Learning

The paper introduces GenAIMMD, an iterative algorithm that learns the committor function and trains a conditioned Boltzmann Generator to generate uncorrelated transition paths without prior knowledge of the reaction coordinate. This method combines transition path sampling with committor learning, enabling fully parallelizable sampling. Benchmarks on a toy model and a polymer system show a substantial performance improvement over standard TPS.

By Maximilian Negedly, Sebastian Falkner, Alessandro Coretti, Christoph Dellago

benchmarks safety

Hugging Face Trending Papers

Sep 17

Correlation-Free Transition Path Sampling through Shooting Point Generation Guided by Committor Learning

Studying the dynamical behavior of a system often depends on characterizing how it transitions between long-lived states. Because such transitions are rare, observing them usually requires specialized...

benchmarks safety

arXiv Machine Learning

Oct 15

Tracking large chemical reaction networks and rare events by neural networks

arXiv:2512.10309v3 Announce Type: replace-cross Abstract: Chemical reaction networks are widely used to model stochastic dynamics in biophysical processes. Their exact probabilistic description via t...

By Jiayu Weng, Xinyi Zhu, Jing Liu, Linyuan L\"u, Pan Zhang, Ying Tang

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 Statistics ML

Oct 2

Langevin-Informed Transfer Learning: Replacing Target Samples by Black-Box Feedback

The paper introduces Langevin-Informed Transfer Learning (LITL), a method that recovers target Langevin dynamics from biased source samples using only black-box feedback. LITL learns the leading spectral structure of the target infinitesimal generator and the projected drift via Dirichlet representation learning, enabling kinetic reconstruction and slow-manifold gradient estimation. The authors provide finite-sample guarantees for eigenvalue, eigenfunction, and drift estimation, and demonstrate LITL’s ability to recover physical transition timescales, build kinetic structure from static generative samples, reconstruct spherical symmetries, and steer latent representations in trained neural networks.

By Vladimir R. Kostic, Karim Lounici, H\'el\`ene Halconruy, Timoth\'ee Devergne, Michele Parrinello, Massimiliano Pontil

diffusion fine-tuning

arXiv Machine Learning

Jun 4

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials

arXiv:2606. 04100v1 Announce Type: new Abstract: Machine learning interatomic potentials (MLIPs) enable efficient and accurate atomistic simulations but depend critically on the quality and diversity of the training data.

By Joanna Zou, Fraser Birks, Dallas Foster, Youssef Marzouk

fine-tuning

arXiv Machine Learning

Jul 1

Learning Hamiltonian Flow Maps: Mean Flow Consistency for Large-Timestep Molecular Dynamics

arXiv:2601. 22123v4 Announce Type: replace Abstract: Simulating the long-time evolution of Hamiltonian systems is limited by the small timesteps required for stable numerical integration.

By Winfried Ripken, Michael Plainer, Gregor Lied, Thorben Frank, Oliver T. Unke, Stefan Chmiela, Frank No\'e, Klaus-Robert M\"uller

arXiv Machine Learning

Oct 15

Langevin Flow Maps: Efficient Molecular Dynamics and Transition Path Sampling

arXiv:2610.05998v2 Announce Type: replace Abstract: Molecular dynamics simulations proceed by integrating the Langevin equations over many small femtosecond timesteps. This poses a challenge for esti...

By Sam McCallum, Niklas Rindtorff, Alexander Tong, James Foster

arXiv Machine Learning

Jun 4

Gradient estimators for parameter inference in discrete stochastic kinetic models

arXiv:2604. 02121v2 Announce Type: replace-cross Abstract: Stochastic kinetic models are ubiquitous in physics, yet inferring their parameters from experimental data remains challenging.

By Ludwig Burger, Annalena Kofler, Lukas Heinrich, Ulrich Gerland

arXiv Machine Learning

Aug 11

Leveraging generative models to assist Monte Carlo sampling

arXiv:2608. 07648v1 Announce Type: cross Abstract: Sampling high-dimensional probability distributions is a central task in scientific computing, with applications ranging from Bayesian inference to statistical physics and molecular simulation.

By Marylou Gabri\'e

diffusion multimodal

Hugging Face Trending Papers

Oct 13

Joint Neural Posterior Estimation and Model Selection for Chemical Reaction Networks

Simulation-based Bayesian inference for Chemical Reaction Networks (CRNs) must simultaneously solve two computationally demanding problems: discriminating between overlapping reaction pathways and est...

arXiv AI

Aug 28

Mechanistic Reaction Prediction via Discrete Flow Matching on Graph-Structured Electron Occupation

MAELLE is a mechanistic reaction prediction framework that models chemical reactions as discrete flow matching over graph-structured electron occupation vectors. It formulates the reactant-to-product mapping as a Continuous-time Markov Chain on electron sites and uses Optimal Transport to generate mechanistically interpretable edit trajectories without elementary step annotations. The method achieves competitive accuracy on the USPTO-480K benchmark, remains robust in out-of-distribution scenarios, and can recover mechanistic pathways that align with known chemistry and predict side products.

By Nguyen Xuan-Vu, Octavian Susanu, Daniel Armstrong, Philippe Schwaller

diffusion benchmarks

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