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Preprint (Author's original)arXiv.org - Non-exclusive license to distribute, Open
Preprint
The fast committor machine: Interpretable prediction with kernels
arXiv.org
16 May 2024
Abstract
In the study of stochastic dynamics, the committor function describes the
probability that a process starting from an initial configuration $x$ will
reach set $A$ before set $B$. This paper introduces a fast and interpretable
method for approximating the committor, called the "fast committor machine"
(FCM). The FCM is based on simulated trajectory data, and it uses this data to
train a kernel model. The FCM identifies low-dimensional subspaces that
optimally describe the $A$ to $B$ transitions, and the subspaces are emphasized
in the kernel model. The FCM uses randomized numerical linear algebra to train
the model with runtime that scales linearly in the number of data points. This
paper applies the FCM to example systems including the alanine dipeptide
miniprotein: in these experiments, the FCM is generally more accurate and
trains more quickly than a neural network with a similar number of parameters.
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Details
- Title
- The fast committor machine: Interpretable prediction with kernels
- Creators
- D AristoffM JohnsonG SimpsonR. J Webber
- Publication Details
- arXiv.org
- Resource Type
- Preprint
- Language
- English
- Academic Unit
- Mathematics
- Other Identifier
- 991021879632004721