# Computing Committor Functions for the Study of Rare Events Using Deep Learning

## TL;DR

This paper presents a deep learning-based method to efficiently compute committor functions, enabling the study of rare transition events in high-dimensional complex systems with rough energy landscapes.

## Contribution

It introduces a novel computational approach combining deep learning, data sampling, and feature engineering to overcome challenges in calculating committor functions for realistic systems.

## Key findings

- Achieves good performance on complex benchmark problems
- Overcomes curse of dimensionality in rare event computation
- Provides an alternative practical method for high-dimensional systems

## Abstract

The committor function is a central object of study in understanding transitions between metastable states in complex systems. However, computing the committor function for realistic systems at low temperatures is a challenging task, due to the curse of dimensionality and the scarcity of transition data. In this paper, we introduce a computational approach that overcomes these issues and achieves good performance on complex benchmark problems with rough energy landscapes. The new approach combines deep learning, data sampling and feature engineering techniques. This establishes an alternative practical method for studying rare transition events between metastable states in complex, high dimensional systems.