Machine learning potential simulations reveal coupled thermodynamic and kinetic control of C(2)H(6)/C(2)H(4) retention in a prototypical ultramicroporous MOF.
- DOI
- 10.1039/d6cp03227c
- Published
- 2026 Nov 10
- Container
- Physical chemistry chemical physics : PCCP
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1039/d6cp03227c,
title = {Machine learning potential simulations reveal coupled thermodynamic and kinetic control of C(2)H(6)/C(2)H(4) retention in a prototypical ultramicroporous MOF.},
author = {Chen J and Tang S and Shi M and Wang X and Xu D},
year = {2026},
journal = {Physical chemistry chemical physics : PCCP},
doi = {10.1039/d6cp03227c},
url = {https://doi.org/10.1039/d6cp03227c}
}RIS
TY - JOUR TI - Machine learning potential simulations reveal coupled thermodynamic and kinetic control of C(2)H(6)/C(2)H(4) retention in a prototypical ultramicroporous MOF. AU - Chen J AU - Tang S AU - Shi M AU - Wang X AU - Xu D PY - 2026 JO - Physical chemistry chemical physics : PCCP DO - 10.1039/d6cp03227c UR - https://doi.org/10.1039/d6cp03227c ER -
APA
J, C., S, T., M, S., X, W., & D, X. (2026). Machine learning potential simulations reveal coupled thermodynamic and kinetic control of C(2)H(6)/C(2)H(4) retention in a prototypical ultramicroporous MOF.. Physical chemistry chemical physics : PCCP. https://doi.org/10.1039/d6cp03227c
Source records
- pubmed · retrieved 2026-11-10T20:37:09.794Z