https://w3id.org/kpxl/ios/ds/np/RA9Vd4kQm9ezjMLZNi31VM2yNGNKR6WSERBokSq7gKYbo#Head https://w3id.org/kpxl/ios/ds/np/RA9Vd4kQm9ezjMLZNi31VM2yNGNKR6WSERBokSq7gKYbo http://www.nanopub.org/nschema#hasAssertion https://w3id.org/kpxl/ios/ds/np/RA9Vd4kQm9ezjMLZNi31VM2yNGNKR6WSERBokSq7gKYbo#assertion https://w3id.org/kpxl/ios/ds/np/RA9Vd4kQm9ezjMLZNi31VM2yNGNKR6WSERBokSq7gKYbo http://www.nanopub.org/nschema#hasProvenance https://w3id.org/kpxl/ios/ds/np/RA9Vd4kQm9ezjMLZNi31VM2yNGNKR6WSERBokSq7gKYbo#provenance https://w3id.org/kpxl/ios/ds/np/RA9Vd4kQm9ezjMLZNi31VM2yNGNKR6WSERBokSq7gKYbo http://www.nanopub.org/nschema#hasPublicationInfo https://w3id.org/kpxl/ios/ds/np/RA9Vd4kQm9ezjMLZNi31VM2yNGNKR6WSERBokSq7gKYbo#pubinfo https://w3id.org/kpxl/ios/ds/np/RA9Vd4kQm9ezjMLZNi31VM2yNGNKR6WSERBokSq7gKYbo http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.nanopub.org/nschema#Nanopublication https://w3id.org/kpxl/ios/ds/np/RA9Vd4kQm9ezjMLZNi31VM2yNGNKR6WSERBokSq7gKYbo#assertion http://id.crossref.org/issn/2451-8492 http://purl.org/dc/terms/title Data Science https://doi.org/10.3233/DS-240063 http://purl.org/dc/terms/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. https://doi.org/10.3233/DS-240063 http://purl.org/dc/terms/date 2024 https://doi.org/10.3233/DS-240063 http://purl.org/dc/terms/hasPart https://w3id.org/kpxl/ios/ds/np/RAZmAIr2d55irsE4M3hGyMk9UXJqPawb8CU92Xj2Nc3QQ https://doi.org/10.3233/DS-240063 http://purl.org/dc/terms/isPartOf http://id.crossref.org/issn/2451-8492 https://doi.org/10.3233/DS-240063 http://purl.org/dc/terms/title Estimating Reaction Barriers with Deep Reinforcement Learning https://doi.org/10.3233/DS-240063 http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/spar/fabio/ResearchPaper https://orcid.org/0009-0005-0705-7768 http://schema.org/affiliation https://ror.org/03yrrjy16 https://orcid.org/0009-0005-0705-7768 http://schema.org/email adpal@imada.sdu.dk https://orcid.org/0009-0005-0705-7768 http://xmlns.com/foaf/0.1/name Adittya Pal https://ror.org/03yrrjy16 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