# Estimating Reaction Barriers with Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2407.12453 · HTML: https://mlsanity.com/paper/2407.12453/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL theory and sample complexity, one of 15 directions in Deep reinforcement learning methods.
- Builds on 15 of its 49 references that are in the map.
- First posted on arXiv 17 Jul 2024.

## Placed in

- Deep reinforcement learning methods / [RL theory and sample complexity](https://mlsanity.com/deeprl/rl-theory-and-sample-complexity/)

## Builds on (most cited first, 15 of 15)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Soft Actor-Critic Algorithms and Applications](https://mlsanity.com/paper/1812.05905/) (2018)
- [Deep Double Descent: Where Bigger Models and More Data Hurt](https://mlsanity.com/paper/1912.02292/) (2019)
- [Understanding and Improving Early Stopping for Learning with Noisy Labels](https://mlsanity.com/paper/2106.15853/) (2021)
- [Neural Map: Structured Memory for Deep Reinforcement Learning](https://mlsanity.com/paper/1702.08360/) (2017)
- [Stochastic Optimal Control for Collective Variable Free Sampling of Molecular Transition Paths](https://mlsanity.com/paper/2207.02149/) (2022)
- [Accurate transition state generation with an object-aware equivariant elementary reaction diffusion model](https://mlsanity.com/paper/2304.06174/) (2023)
- [Teaching a Machine to Read Maps with Deep Reinforcement Learning](https://mlsanity.com/paper/1711.07479/) (2017)
- [Deep Reinforcement Learning of Transition States](https://mlsanity.com/paper/2011.06700/) (2020)
- [To the Max: Reinventing Reward in Reinforcement Learning](https://mlsanity.com/paper/2402.01361/) (2024)
- [Nudged elastic band calculations accelerated with Gaussian process regression](https://mlsanity.com/paper/1706.04606/) (2017)
- [Deep Reinforcement Learning in Finite-Horizon to Explore the Most Probable Transition Pathway](https://mlsanity.com/paper/2304.12994/) (2023)
- [Optimizing thermodynamic trajectories using evolutionary and gradient-based reinforcement learning](https://mlsanity.com/paper/1903.08543/) (2019)
- [Actor-Critic Algorithm for High-dimensional Partial Differential Equations](https://mlsanity.com/paper/2010.03647/) (2020)

## Built on by (most cited first, 1 of 1)

- [Chem-NMF: Multi-layer α-divergence Non-Negative Matrix Factorization for Cardiorespiratory Disease Clustering, with Improved Convergence Inspired by Chemical Catalysts and Rigorous Asymptotic Analysis](https://mlsanity.com/paper/2510.06632/) (2025)

## Abstract (arXiv)

Stable states in complex systems correspond to local minima on the associated potential energy surface. Transitions between these local minima govern the dynamics of such systems. Precisely determining the transition pathways in complex and high-dimensional systems is challenging because these transitions are rare events, and isolating the relevant species in experiments is difficult. Most of the time, the system remains near a local minimum, with rare, large fluctuations leading to transitions between minima. The probability of such transitions decreases exponentially with the height of the energy barrier, making the system's dynamics highly sensitive to the calculated energy barriers. This work aims to formulate the problem of finding the minimum energy barrier between two stable states in the system's state space as a cost-minimization problem. We propose solving this problem using reinforcement learning algorithms. The exploratory nature of reinforcement learning agents enables efficient sampling and determination of the minimum energy barrier for transitions.