Data-Driven Approach for Predicting Long-Time Anomalous Transport from Short Trajectories.

Liu C, Guan Y, Li J, Su M

DOI: 10.1021/acs.jctc.6c01846
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.6c01846,
  title = {Data-Driven Approach for Predicting Long-Time Anomalous Transport from Short Trajectories.},
  author = {Liu C and Guan Y and Li J and Su M},
  year = {2026},
  journal = {Journal of chemical theory and computation},
  doi = {10.1021/acs.jctc.6c01846},
  url = {https://doi.org/10.1021/acs.jctc.6c01846}
}

RIS

TY  - JOUR
TI  - Data-Driven Approach for Predicting Long-Time Anomalous Transport from Short Trajectories.
AU  - Liu C
AU  - Guan Y
AU  - Li J
AU  - Su M
PY  - 2026
JO  - Journal of chemical theory and computation
DO  - 10.1021/acs.jctc.6c01846
UR  - https://doi.org/10.1021/acs.jctc.6c01846
ER  -

APA

C, L., Y, G., J, L., & M, S. (2026). Data-Driven Approach for Predicting Long-Time Anomalous Transport from Short Trajectories.. Journal of chemical theory and computation. https://doi.org/10.1021/acs.jctc.6c01846

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pubmed · retrieved 2026-11-10T18:01:47.966Z