Machine learning-enabled prediction of nonequilibrium reaction dynamics: A mixed Gaussian process regression-neural network framework for O + O2 state-to-state dissociation kinetics.
- DOI
- 10.1063/5.0296033
- Published
- 2025 Nov 7
- Container
- The Journal of chemical physics
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1063/5.0296033,
title = {Machine learning-enabled prediction of nonequilibrium reaction dynamics: A mixed Gaussian process regression-neural network framework for O + O2 state-to-state dissociation kinetics.},
author = {Yun S and Yang J and Li J},
year = {2025},
journal = {The Journal of chemical physics},
doi = {10.1063/5.0296033},
url = {https://doi.org/10.1063/5.0296033}
}RIS
TY - JOUR TI - Machine learning-enabled prediction of nonequilibrium reaction dynamics: A mixed Gaussian process regression-neural network framework for O + O2 state-to-state dissociation kinetics. AU - Yun S AU - Yang J AU - Li J PY - 2025 JO - The Journal of chemical physics DO - 10.1063/5.0296033 UR - https://doi.org/10.1063/5.0296033 ER -
APA
S, Y., J, Y., & J, L. (2025). Machine learning-enabled prediction of nonequilibrium reaction dynamics: A mixed Gaussian process regression-neural network framework for O + O2 state-to-state dissociation kinetics.. The Journal of chemical physics. https://doi.org/10.1063/5.0296033
Source records
- pubmed · retrieved 2026-09-25T05:39:32.677Z