Article: Estimating Reaction Barriers with Deep Reinforcement Learning

Full identifier: https://w3id.org/np/RAP9lmaJ2xhyhQarVz_u1roxFm9Shu_Z9pp7BNYak_DCc

 Article: Estimating Reaction Barriers with Deep Reinforcement Learning DataScience ResearchPaper ScholarlyWork ▼ comment approve/disapprove edit as derived nanopublication create new with same template

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https://doi.org/10.3233/DS-240063 doi:10.3233/DS-240063 http://purl.org/dc/terms/title has the title (this is a literal) "Estimating Reaction Barriers with Deep Reinforcement Learning" .

https://doi.org/10.3233/DS-240063 doi:10.3233/DS-240063 http://purl.org/dc/terms/abstract has the abstract (this is a literal) "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." .

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Adittya Pal (author), Tobias Kuhn (creator), 4 Oct 2024, 09:39:20 UTC

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