Class: Estimating Reaction Barriers with Deep Reinforcement Learning (explore)

Class: Estimating Reaction Barriers with Deep Reinforcement Learning

Full identifier: https://w3id.org/np/RA0JAKZR_z1jwkGeEVFh4rApkHM8CDGX2PJqXSpty3iKM

Status

Nanopublication

Class: Estimating Reaction Barriers with Deep Reinforcement Learning Class Draft comment approve/disapprove edit as derived nanopublication create new with same template

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"Estimating Reaction Barriers with Deep Reinforcement Learning"

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"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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algorithm

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reinforcement learning

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0009-0005-0705-7768

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Attribution 4.0 International (CC BY 4.0)

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"Class: Estimating Reaction Barriers with Deep Reinforcement Learning"

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"2024-09-29T19:14:24.257Z"

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reinforcement learning

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0009-0005-0705-7768

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nanodash.knowledgepixels.com

0009-0005-0705-7768, 29 Sep 2024, 19:14:24 UTC

References

Nanopublication	Part	Subject	Predicate	Object	Published By	Published On
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https://w3id.org/np/RAgOeYsOZ9... RAgOeYsOZ9	links a nanopublication to its pubinfo http://www.nanopub.org/nschema#hasPublicationInfo pubinfo	https://w3id.org/np/RAgOeYsOZ9... RAgOeYsOZ9	http://www.w3.org/ns/prov#wasDerivedFrom wasDerivedFrom	RA0JAKZR_z	(unknown)	2024-10-02T09:51:13.183Z
https://w3id.org/np/RA6kCn01DW... RA6kCn01DW	links a nanopublication to its pubinfo http://www.nanopub.org/nschema#hasPublicationInfo pubinfo	https://w3id.org/np/RA6kCn01DW... RA6kCn01DW	http://www.w3.org/ns/prov#wasDerivedFrom wasDerivedFrom	RA0JAKZR_z	(unknown)	2024-10-02T09:46:03.381Z

Raw

TriG(txt), JSON-LD(txt), N-Quads(txt), XML(txt)