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J. Weare

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#machine learning Review Sep 2026

Machine learning kinetics from molecular dynamics data

A common operator viewpoint is developed connecting generator-based partial differential equations, variational principles, Markov state models, dynamical Galerkin approximation, and neural networks with an emphasis on self-supervised methods that learn solutions of their defining dynamical equations rather than relyin...

J. Weare, Aaron R. Dinner · 1 citation
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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