A novel method that integrates reinforcement learning with robotics planning to chart low-energy molecular transition pathways, employing enhanced sampling techniques.

-Development of a framework combining reinforcement learning with robotics planning for molecular dynamics analysis

-Application of Jarzynski’s equality and stiff-spring approximation for accurate energy estimations at the atomic level

-Implementation of policy-driven adaptive steered molecular dynamics (SMD), a first in this field

-Proven effectiveness in identifying transition pathways in complex molecular systems, demonstrated through alanine dipeptide case study