The fast committor machine: Interpretable prediction with kernels

D. Aristoff; M. Johnson; G. Simpson; R. J. Webber

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arXiv · 2405.10410

The fast committor machine: Interpretable prediction with kernels

D. Aristoff·M. Johnson·G. Simpson·R. J. Webber

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Abstract

In the study of stochastic systems, the committor function describes the probability that a system starting from an initial configuration $x$ will reach a set $B$ before a set $A$. This paper introduces an efficient and interpretable algorithm for approximating the committor, called the "fast committor machine" (FCM). The FCM uses simulated trajectory data to build a kernel-based model of the committor. The kernel function is constructed to emphasize low-dimensional subspaces that optimally describe the $A$ to $B$ transitions. The coefficients in the kernel model are determined using randomized linear algebra, leading to a runtime that scales linearly in the number of data points. In numerical experiments involving a triple-well potential and alanine dipeptide, the FCM yields higher accuracy and trains more quickly than a neural network with the same number of parameters. The FCM is also more interpretable than the neural net.

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BibTeX RIS

D. Aristoff, M. Johnson, G. Simpson, R. J. Webber. 2024-05-16. The fast committor machine: Interpretable prediction with kernels. https://arxiv.org/abs/2405.10410

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