mlsanityData to 14 Oct 2026

Estimating Reaction Barriers with Deep Reinforcement Learning

arXiv 2407.12453 ↗First posted 17 Jul 2024cs.LG physics.comp-phcited by 1

On the map: Deep reinforcement learning methods → RL theory and sample complexity

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.

Key facts. Cited by 1 paper 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.

Builds on

15 of 49 references.

References · 15

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  2. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
  3. 2018Soft Actor-Critic Algorithms and Applicationscited by 1,240
  4. 2019Deep Double Descent: Where Bigger Models and More Data Hurtcited by 672
  5. 2021Understanding and Improving Early Stopping for Learning with Noisy Labelscited by 113
  6. 2017Neural Map: Structured Memory for Deep Reinforcement Learningcited by 90
  7. 2022Stochastic Optimal Control for Collective Variable Free Sampling of Molecular Transition Pathscited by 38
  8. 2023Accurate transition state generation with an object-aware equivariant elementary reaction diffusion modelcited by 30
  9. 2017Teaching a Machine to Read Maps with Deep Reinforcement Learningcited by 9
  10. 2020Deep Reinforcement Learning of Transition Statescited by 8
  11. 2024To the Max: Reinventing Reward in Reinforcement Learningcited by 8
  12. 2017Nudged elastic band calculations accelerated with Gaussian process regressioncited by 7
3 more
  1. 2023Deep Reinforcement Learning in Finite-Horizon to Explore the Most Probable Transition Pathwaycited by 5
  2. 2019Optimizing thermodynamic trajectories using evolutionary and gradient-based reinforcement learningcited by 3
  3. 2020Actor-Critic Algorithm for High-dimensional Partial Differential Equationscited by 1

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Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Chem-NMF: Multi-layer α-divergence Non-Negative Matrix Factorization for Cardiorespiratory Disease Clustering, with Improved Convergence Inspired by Chemical Catalysts and Rigorous Asymptotic Analysiscited by 1

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  1. 2024Computing Transition Pathways for the Study of Rare Events Using Deep Reinforcement Learning
  2. 2026Bridging Control, Inference, Transport, and Thermodynamics: From Theory to Applications in Learning
  3. 2025Efficient Exploration of Chemical Kinetics
  4. 2023Contrastive Energy Prediction for Exact Energy-Guided Diffusion Sampling in Offline Reinforcement Learning
  5. 2024Sampling from Energy-based Policies using Diffusion
  6. 2026Bridging Dynamics Gaps via Diffusion Schr"odinger Bridge for Cross-Domain Reinforcement Learning
  7. 2026How Does the Lagrangian Guide Safe Reinforcement Learning through Diffusion Models?
  8. 2024Generalized Flow Matching for Transition Dynamics Modeling