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

arXiv: https://arxiv.org/abs/1906.06285 · HTML: https://mlsanity.com/paper/1906.06285/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2019.

## Built on by (most cited first, 29 of 29)

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- [A Finite Expression Method for Solving High-Dimensional Committor Problems](https://mlsanity.com/paper/2306.12268/) (2023)
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- [Supervised Learning and the Finite-Temperature String Method for Computing Committor Functions and Reaction Rates](https://mlsanity.com/paper/2107.13522/) (2021)
- [Deep Learning Method for Computing Committor Functions with Adaptive Sampling](https://mlsanity.com/paper/2404.06206/) (2024)
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- [Computing the Invariant Distribution of Randomly Perturbed Dynamical Systems Using Deep Learning](https://mlsanity.com/paper/2110.11538/) (2021)
- [Estimating Committor Functions via Deep Adaptive Sampling on Rare Transition Paths](https://mlsanity.com/paper/2501.15522/) (2025)
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- [SCARCE: Scalable Cascade Analysis for Rare-event Characterisation via Embeddings](https://mlsanity.com/paper/2606.29623/) (2026)
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- [An exact multiple-time-step variational formulation for the committor and the transition rate](https://mlsanity.com/paper/2509.03539/) (2025)
- [Computing Transition Pathways for the Study of Rare Events Using Deep Reinforcement Learning](https://mlsanity.com/paper/2404.05905/) (2024)
- [Accelerated Bayesian Optimal Experimental Design via Conditional Density Estimation and Informative Data](https://mlsanity.com/paper/2507.15235/) (2025)
- [Nonparametric Reaction Coordinate Optimization with Histories: A Framework for Rare Event Dynamics](https://mlsanity.com/paper/2508.07326/) (2025)
- [FMint-SDE: A Multimodal Foundation Model for Accelerating Numerical Simulation of SDEs via Error Correction](https://mlsanity.com/paper/2510.27173/) (2025)
- [A Priori Sampling of Transition States with Guided Diffusion](https://mlsanity.com/paper/2603.25980/) (2026)
- [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 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.