RT Journal Article
SR Electronic
T1 Learning Free Energy Pathways through Reinforcement Learning of Adaptive Steered Molecular Dynamics
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.10.04.510845
DO 10.1101/2022.10.04.510845
A1 Ho, Nicholas
A1 Cava, John Kevin
A1 Vant, John
A1 Shukla, Ankita
A1 Miratsky, Jake
A1 Turaga, Pavan
A1 Maciejewski, Ross
A1 Singharoy, Abhishek
YR 2022
UL http://biorxiv.org/content/early/2022/10/05/2022.10.04.510845.abstract
AB 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 Jarzynski’s 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.Competing Interest StatementThe authors have declared no competing interest.