Deep Learning Method for Computing Committor Functions with Adaptive Sampling

TL;DR

This paper introduces a deep learning approach with two adaptive sampling schemes for efficiently computing committor functions in high-dimensional dynamical systems, improving data collection for complex transition analysis.

Contribution

The work presents two novel adaptive sampling schemes that actively generate data using a learned bias potential, enhancing the computation of committor functions in complex systems.

Findings

Sampling scheme II produces uniformly distributed data along transition pathways.

The method effectively computes committor functions in high-dimensional systems.

Demonstrated success on alanine dipeptide and solvated dimer systems.

Abstract

The committor function is a central object for quantifying the transitions between metastable states of dynamical systems. Recently, a number of computational methods based on deep neural networks have been developed for computing the high-dimensional committor function. The success of the methods relies on sampling adequate data for the transition, which still is a challenging task for complex systems at low temperatures. In this work, we propose a deep learning method with two novel adaptive sampling schemes (I and II). In the two schemes, the data are generated actively with a modified potential where the bias potential is constructed from the learned committor function. We theoretically demonstrate the advantages of the sampling schemes and show that the data in sampling scheme II are uniformly distributed along the transition tube. This makes a promising method for studying the transition of complex systems. The efficiency of the method is illustrated in high-dimensional systems including the alanine dipeptide and a solvated dimer system.