# Deep Learning Method for Computing Committor Functions with Adaptive Sampling

arXiv: https://arxiv.org/abs/2404.06206 · HTML: https://mlsanity.com/paper/2404.06206/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 29 references that are in the map.
- First posted on arXiv 9 Apr 2024.

## Builds on (most cited first, 4 of 4)

- [SchNet: A continuous-filter convolutional neural network for modeling quantum interactions](https://mlsanity.com/paper/1706.08566/) (2017)
- [Solving for high dimensional committor functions using artificial neural networks](https://mlsanity.com/paper/1802.10275/) (2018)
- [Computing Committor Functions for the Study of Rare Events Using Deep Learning](https://mlsanity.com/paper/1906.06285/) (2019)
- [Supervised Learning and the Finite-Temperature String Method for Computing Committor Functions and Reaction Rates](https://mlsanity.com/paper/2107.13522/) (2021)

## Built on by (most cited first, 5 of 5)

- [Estimating Committor Functions via Deep Adaptive Sampling on Rare Transition Paths](https://mlsanity.com/paper/2501.15522/) (2025)
- [Rare Event Analysis via Stochastic Optimal Control](https://mlsanity.com/paper/2604.13213/) (2026)
- [Error Bounds for Physics-Informed Neural Networks in Fokker-Planck PDEs](https://mlsanity.com/paper/2410.22371/) (2024)
- [Reactive Flux Matching: Mechanism Discovery and Adaptive Sampling of Rare Events](https://mlsanity.com/paper/2606.06295/) (2026)
- [Machine learning kinetics from molecular dynamics data](https://mlsanity.com/paper/2609.17736/) (2026)

## Abstract (arXiv)

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.