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The fast committor machine: Interpretable prediction with kernels.
- Source :
-
Journal of Chemical Physics . 8/28/2024, Vol. 161 Issue 8, p1-11. 11p. - Publication Year :
- 2024
-
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 with 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. [ABSTRACT FROM AUTHOR]
- Subjects :
- *STOCHASTIC systems
*KERNEL functions
*LINEAR algebra
*ALANINE
*PROBABILITY theory
Subjects
Details
- Language :
- English
- ISSN :
- 00219606
- Volume :
- 161
- Issue :
- 8
- Database :
- Academic Search Index
- Journal :
- Journal of Chemical Physics
- Publication Type :
- Academic Journal
- Accession number :
- 179372621
- Full Text :
- https://doi.org/10.1063/5.0222798