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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.
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...
The paper introduces Align-React, a chemical reaction representation learning framework that incorporates atomic correspondence between reactants and products, an adapter for embedding reaction conditions, and a Reaction-Center-Aware attention mechanism. These components enable the model to capture precise molecular transformations and focus on critical functional groups, leading to improved performance across a variety of organic reaction tasks. The framework outperforms existing architectures on most benchmark datasets.
ChemBART is a BART‑based language model trained on holistic reaction expressions (Reactant > Reagent > Product) using a mask‑filling objective. It can solve diverse chemical tasks such as precursor/reagent generation, temperature‑yield regression, and property classification, and it is integrated with Monte Carlo tree search to design multi‑step synthesis routes. The model’s predictions were experimentally validated, yielding shorter synthetic pathways with over 20 % higher yield than literature benchmarks.
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:2609.14569v1 Announce Type: cross Abstract: Constraining reaction rate coefficients is a central challenge in the development of explicit atmospheric chemical mechanisms, particularly for autox...