Generalized Machine Learning Potentials for Predicting Low-Pressure Water Adsorption in Flexible Al-Based Metal-Organic Frameworks.

Li Y, Zhang X, Jin X, Smit B

DOI: 10.1021/acs.jctc.6c01162
Published: 2026 Sep 8
Container: Journal of chemical theory and computation
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.1021/acs.jctc.6c01162,
  title = {Generalized Machine Learning Potentials for Predicting Low-Pressure Water Adsorption in Flexible Al-Based Metal-Organic Frameworks.},
  author = {Li Y and Zhang X and Jin X and Smit B},
  year = {2026},
  journal = {Journal of chemical theory and computation},
  doi = {10.1021/acs.jctc.6c01162},
  url = {https://doi.org/10.1021/acs.jctc.6c01162}
}

RIS

TY  - JOUR
TI  - Generalized Machine Learning Potentials for Predicting Low-Pressure Water Adsorption in Flexible Al-Based Metal-Organic Frameworks.
AU  - Li Y
AU  - Zhang X
AU  - Jin X
AU  - Smit B
PY  - 2026
JO  - Journal of chemical theory and computation
DO  - 10.1021/acs.jctc.6c01162
UR  - https://doi.org/10.1021/acs.jctc.6c01162
ER  -

APA

Y, L., X, Z., X, J., & B, S. (2026). Generalized Machine Learning Potentials for Predicting Low-Pressure Water Adsorption in Flexible Al-Based Metal-Organic Frameworks.. Journal of chemical theory and computation. https://doi.org/10.1021/acs.jctc.6c01162

Source records

pubmed · retrieved 2026-09-26T01:19:41.252Z