Local Correlation-Based Machine Learning Enables Coupled-Cluster-Level Energetics for Thousand-Atom Molecular Systems.

Feng H, Li S, Ma J, Li W

DOI: 10.1021/acs.jctc.6c01569
Published: 2026 Nov 10
Container: Journal of chemical theory and computation
Publisher: Not recorded
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1021/acs.jctc.6c01569,
  title = {Local Correlation-Based Machine Learning Enables Coupled-Cluster-Level Energetics for Thousand-Atom Molecular Systems.},
  author = {Feng H and Li S and Ma J and Li W},
  year = {2026},
  journal = {Journal of chemical theory and computation},
  doi = {10.1021/acs.jctc.6c01569},
  url = {https://doi.org/10.1021/acs.jctc.6c01569}
}

RIS

TY  - JOUR
TI  - Local Correlation-Based Machine Learning Enables Coupled-Cluster-Level Energetics for Thousand-Atom Molecular Systems.
AU  - Feng H
AU  - Li S
AU  - Ma J
AU  - Li W
PY  - 2026
JO  - Journal of chemical theory and computation
DO  - 10.1021/acs.jctc.6c01569
UR  - https://doi.org/10.1021/acs.jctc.6c01569
ER  -

APA

H, F., S, L., J, M., & W, L. (2026). Local Correlation-Based Machine Learning Enables Coupled-Cluster-Level Energetics for Thousand-Atom Molecular Systems.. Journal of chemical theory and computation. https://doi.org/10.1021/acs.jctc.6c01569

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pubmed · retrieved 2026-11-10T21:43:25.400Z