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- DS-240063 type ResearchPaper assertion.
- author-list _1 0009-0005-0705-7768 assertion.
- DS-240063 isPartOf 2451-8492 assertion.
- DS-240063 hasPart RAZmAIr2d55irsE4M3hGyMk9UXJqPawb8CU92Xj2Nc3QQ assertion.
- 2451-8492 title "Data Science" assertion.
- DS-240063 title "Estimating Reaction Barriers with Deep Reinforcement Learning" assertion.
- 0009-0005-0705-7768 name "Adittya Pal" assertion.
- 03yrrjy16 name "Institut for Matematik og Datalogi, Syddansk Universitet, Campusvej 55, 5230 Odense M, Denmark" assertion.
- DS-240063 date "2024" assertion.
- DS-240063 abstract "Stable states in complex systems correspond to local minima on the associated potential energy surface. Transitions between these local minima govern the dynamics of such systems. Precisely determining the transition pathways in complex and high-dimensional systems is challenging because these transitions are rare events, and isolating the relevant species in experiments is difficult. Most of the time, the system remains near a local minimum, with rare, large fluctuations leading to transitions between minima. The probability of such transitions decreases exponentially with the height of the energy barrier, making the system's dynamics highly sensitive to the calculated energy barriers. This work aims to formulate the problem of finding the minimum energy barrier between two stable states in the system's state space as a cost-minimization problem. It is proposed to solve this problem using reinforcement learning algorithms. The exploratory nature of reinforcement learning agents enables efficient sampling and determination of the minimum energy barrier for transitions." assertion.
- 0009-0005-0705-7768 email "adpal@imada.sdu.dk" assertion.
- 0009-0005-0705-7768 affiliation 03yrrjy16 assertion.