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#machine learning Review Sep 2026

Machine learning kinetics from molecular dynamics data

Most molecular transitions occur on timescales far beyond direct molecular dynamics simulations. The committor, the probability that a configuration reaches a product state before a reactant state, is a central kinetic statistic, providing a mechanism-independent reaction coordinate and a foundation for transition path...

J. Weare, Aaron R. Dinner · 0 citations
Preprint Jul 2026

Nuclear Quantum Effects as a Denoising Problem

Nuclear quantum effects are rigorously captured by imaginary-time path integrals, which map the quantum Boltzmann distribution onto a ring polymer of classical replicas. Yet the nuclear masses, the coupling to the environment, and the boundary conditions of the path remain hard-wired in the simulation or the trained mo...

Weizhou Wang, J. Weare, Aaron R. Dinner · 0 citations

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