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Learning Free Energy Pathways through Reinforcement Learning of Adaptive Steered Molecular Dynamics
Nicholas Ho · John Kevin Cava · John Vant · Ankita Shukla · Jacob Miratsky · Pavan Turaga · Ross Maciejewski · Abhishek Singharoy
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 stiff-spring 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.
Author Information
Nicholas Ho (Arizona State University)
John Kevin Cava (Arizona State University)
John Vant (Arizona State University)
Ankita Shukla (ASU)
Jacob Miratsky (asu)
Pavan Turaga (Arizona State University)
Ross Maciejewski (Arizona State University)
Abhishek Singharoy (Arizona State University)
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