Learning Free Energy Pathways through Reinforcement Learning of Adaptive Steered Molecular Dynamics
Ho, N.; Cava, J. K.; Vant, J.; Shukla, A.; Miratsky, J.; Turaga, P.; Maciejewski, R.; Singharoy, A.
Show abstract
In this paper, we develop a formulation to utilize reinforcement learning and sampling-based robotics planning to derive low free energy transition pathways between two known states. Our formulation uses Jarzynskis equality and the stiffspring approximation to obtain point estimates of energy, and construct an informed path search with atomistic resolution. At the core of this framework, is our first ever attempt we use a policy driven adaptive steered molecular dynamics (SMD) to control our molecular dynamics simulations. We show that both the reinforcement learning and robotics planning realization of the RL-guided framework can solve for pathways on toy analytical surfaces and alanine dipeptide.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Fluid-Derived Lattices for Unbiased Modeling of Bacterial Colony Growth 95%
- Collective Evolution Learning Model for Vision-Based Collective Motion with Collision Avoidance 94%
- A Hessian-based decomposition characterizes how performance in complex motor skills depends on individual strategy and variability 94%
Similar papers in this journal
Similar papers in this journal
- Inferring DNA kinkability from biased MD simulations 93%
- An Orientationally Averaged Version of the Rotne-Prager-Yamakawa Tensor Provides A Fast But Still Accurate Treatment Of Hydrodynamic Interactions In Brownian Dynamics Simulations Of Biological Macromolecules 93%
- AlphaMut: a deep reinforcement learning model to suggest helix-disrupting mutations 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.