PT  - JOURNAL ARTICLE
AU  - Nicholas Ho
AU  - John Kevin Cava
AU  - John Vant
AU  - Ankita Shukla
AU  - Jake Miratsky
AU  - Pavan Turaga
AU  - Ross Maciejewski
AU  - Abhishek Singharoy
TI  - Learning Free Energy Pathways through Reinforcement Learning of Adaptive Steered Molecular Dynamics
AID  - 10.1101/2022.10.04.510845
DP  - 2022 Jan 01
TA  - bioRxiv
PG  - 2022.10.04.510845
4099  - http://biorxiv.org/content/early/2022/10/05/2022.10.04.510845.short
4100  - http://biorxiv.org/content/early/2022/10/05/2022.10.04.510845.full
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.