The fast committor machine: Interpretable prediction with kernels

• URL: http://arxiv.org/abs/2405.10410v3
• Date: Sat, 10 Aug 2024 22:20:10 GMT
• Title: The fast committor machine: Interpretable prediction with kernels
• Authors: D. Aristoff, M. Johnson, G. Simpson, R. J. Webber,
• Abstract summary: This paper introduces an efficient algorithm for approximating the committor, called the "fast committor machine" (FCM) The kernel function is constructed to emphasize low-dimensional subspaces that optimally describe the $A$ to $B$ transitions. The FCM yields higher accuracy and trains more quickly than a neural network with the same number of parameters.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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