Machine Learning-Driven Phase Field Numerical Analysis for a Comprehensive Theoretical Framework of Local Electrochemical Environment and Operating Conditions Effects on Dendrite Evolution Mechanisms
- DOI
- 10.1021/acs.nanolett.6c03313
- Published
- 2026-09-16
- Container
- Nano Letters
- Publisher
- American Chemical Society (ACS)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1021/acs.nanolett.6c03313,
title = {Machine Learning-Driven
Phase Field Numerical Analysis
for a Comprehensive Theoretical Framework of Local Electrochemical
Environment and Operating Conditions Effects on Dendrite Evolution
Mechanisms},
author = {Tengxiao Mu and Shenglong Huang and Yilun Cheng and Zizhao Ding and Chao Jiang},
year = {2026},
journal = {Nano Letters},
doi = {10.1021/acs.nanolett.6c03313},
url = {https://doi.org/10.1021/acs.nanolett.6c03313}
}RIS
TY - JOUR TI - Machine Learning-Driven Phase Field Numerical Analysis for a Comprehensive Theoretical Framework of Local Electrochemical Environment and Operating Conditions Effects on Dendrite Evolution Mechanisms AU - Tengxiao Mu AU - Shenglong Huang AU - Yilun Cheng AU - Zizhao Ding AU - Chao Jiang PY - 2026 JO - Nano Letters DO - 10.1021/acs.nanolett.6c03313 UR - https://doi.org/10.1021/acs.nanolett.6c03313 ER -
APA
Mu, T., Huang, S., Cheng, Y., Ding, Z., & Jiang, C. (2026). Machine Learning-Driven Phase Field Numerical Analysis for a Comprehensive Theoretical Framework of Local Electrochemical Environment and Operating Conditions Effects on Dendrite Evolution Mechanisms. Nano Letters. https://doi.org/10.1021/acs.nanolett.6c03313
Source records
- crossref · retrieved 2026-11-09T18:52:42.713Z