doi:10.3233/DS-240063

Full identifier: https://doi.org/10.3233/DS-240063

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References

Nanopublication Part Subject Predicate Object Published By Published On
links a nanopublication to its assertion http://www.nanopub.org/nschema#hasAssertion assertion
doi:10.3233/DS-240063
Tobias Kuhn
2024-10-04T09:40:38.275Z
links a nanopublication to its assertion http://www.nanopub.org/nschema#hasAssertion assertion
doi:10.3233/DS-240063
Estimating Reaction Barriers with Deep Reinforcement Learning
Tobias Kuhn
2024-10-04T09:40:38.275Z
links a nanopublication to its assertion http://www.nanopub.org/nschema#hasAssertion assertion
doi:10.3233/DS-240063
2024
Tobias Kuhn
2024-10-04T09:40:38.275Z
links a nanopublication to its assertion http://www.nanopub.org/nschema#hasAssertion assertion
doi:10.3233/DS-240063
Tobias Kuhn
2024-10-04T09:40:38.275Z
links a nanopublication to its assertion http://www.nanopub.org/nschema#hasAssertion assertion
doi:10.3233/DS-240063
Tobias Kuhn
2024-10-04T09:40:38.275Z
links a nanopublication to its assertion http://www.nanopub.org/nschema#hasAssertion assertion
doi:10.3233/DS-240063
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.
Tobias Kuhn
2024-10-04T09:40:38.275Z
links a nanopublication to its pubinfo http://www.nanopub.org/nschema#hasPublicationInfo pubinfo
doi:10.3233/DS-240063
Tobias Kuhn
2024-10-04T09:40:38.275Z
links a nanopublication to its provenance http://www.nanopub.org/nschema#hasProvenance provenance
doi:10.3233/DS-240063
Tobias Kuhn
2024-10-04T09:20:40.120Z
links a nanopublication to its pubinfo http://www.nanopub.org/nschema#hasPublicationInfo pubinfo
doi:10.3233/DS-240063
Tobias Kuhn
2024-10-04T09:20:40.120Z
links a nanopublication to its provenance http://www.nanopub.org/nschema#hasProvenance provenance
doi:10.3233/DS-240063
Tobias Kuhn
2024-10-04T09:20:40.120Z

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