# Reinforcement Learning for Molecular Dynamics Optimization: A Stochastic Pontryagin Maximum Principle Approach

arXiv: https://arxiv.org/abs/2212.03320 · HTML: https://mlsanity.com/paper/2212.03320/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 28 references that are in the map.
- First posted on arXiv 6 Dec 2022.

## Builds on (most cited first, 3 of 3)

- [A Differential Equation for Modeling Nesterov's Accelerated Gradient Method: Theory and Insights](https://mlsanity.com/paper/1503.01243/) (2015)
- [ProteinNet: a standardized data set for machine learning of protein structure](https://mlsanity.com/paper/1902.00249/) (2019)
- [Prediction of amino acid side chain conformation using a deep neural network](https://mlsanity.com/paper/1707.08381/) (2017)

## Built on by (most cited first, 2 of 2)

- [PHAST: Port-Hamiltonian Architecture for Structured Temporal Dynamics Forecasting](https://mlsanity.com/paper/2602.17998/) (2026)
- [A Differential and Pointwise Control Approach to Reinforcement Learning](https://mlsanity.com/paper/2404.15617/) (2024)

## Abstract (arXiv)

In this paper, we present a novel reinforcement learning framework designed to optimize molecular dynamics by focusing on the entire trajectory rather than just the final molecular configuration. Leveraging a stochastic version of Pontryagin's Maximum Principle (PMP) and Soft Actor-Critic (SAC) algorithm, our framework effectively explores non-convex molecular energy landscapes, escaping local minima to stabilize in low-energy states. Our approach operates in continuous state and action spaces without relying on labeled data, making it applicable to a wide range of molecular systems. Through extensive experimentation on six distinct molecules, including Bradykinin and Oxytocin, we demonstrate competitive performance against other unsupervised physics-based methods, such as the Greedy and NEMO-based algorithms. Our method's adaptability and focus on dynamic trajectory optimization make it suitable for applications in areas such as drug discovery and molecular design.