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arXiv Machine Learning By Tiejun Li, Zhaoqi Liu, Xiang Zhou

Iterative Neural Variational Solver of Committor Function in Under-damped Langevin Dynamics

Read the original on arXiv Machine Learning →

The paper introduces an iterative neural variational solver for the committor function in under‑damped Langevin dynamics. By splitting the infinitesimal generator into self‑adjoint and skew‑adjoint parts, each subproblem gains a variational form that can be efficiently solved with neural networks, resembling a generalized Concus‑Golub‑Widlund splitting. The method is validated on benchmark systems such as a double‑well potential, the Muller‑Brown potential, and alanine dipeptide, demonstrating practical accuracy and extensibility for studying rare transition events across different damping coefficients.

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