Reinforcement learning for traversing chemical structure space: Optimizing transition states and minimum energy paths of molecules
arXiv:2310.03511 · doi:10.1021/acs.jpclett.3c02771
Abstract
In recent years, deep learning has made remarkable strides, surpassing human capabilities in tasks like strategy games, and it has found applications in complex domains, including protein folding. In the realm of quantum chemistry, machine learning methods have primarily served as predictive tools or design aids using generative models, while reinforcement learning remains in its early stages of exploration. This work introduces an actor-critic reinforcement learning framework suitable for diverse optimization tasks, such as searching for molecular structures with specific properties within conformational spaces. As an example, we show an implementation of this scheme for calculating minimum energy pathways of a Claisen rearrangement reaction and a number of SN2 reactions. Our results show that the algorithm is able to accurately predict minimum energy pathways and thus, transition states, therefore providing the first steps in using actor-critic methods to study chemical reactions.
arXiv comment30 pages, 10 figures
References in corpus (21)
- A Brief Survey of Deep Reinforcement Learning
- SchNet - a deep learning architecture for molecules and materials
- ANI-1: An extensible neural network potential with DFT accuracy at force field computational cost
- E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials
- Deep Reinforcement Learning for De-Novo Drug Design
- PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments and Partial Charges
- Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds
- Machine learning for electronically excited states of molecules
- SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effects
- A Transferable Machine-Learning Model of the Electron Density
- Combining SchNet and SHARC: The SchNarc machine learning approach for excited-state dynamics
- A Survey on Evaluation of Large Language Models
- Challenges and Applications of Large Language Models
- High-throughput property-driven generative design of functional organic molecules
- Deep Learning for UV Absorption Spectra with SchNarc: First Steps Towards Transferability in Chemical Compound Space
- Physically inspired deep learning of molecular excitations and photoemission spectra
- Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
- Actor-Critic Method for High Dimensional Static Hamilton--Jacobi--Bellman Partial Differential Equations based on Neural Networks
- Deep Reinforcement Learning of Transition States
- Accurate transition state generation with an object-aware equivariant elementary reaction diffusion model
- Actor-Critic Algorithm for High-dimensional Partial Differential Equations