Machine learning force fields for inorganic crystalline materials: principles, advances, and emerging applications.
- DOI
- 10.1039/d6cp01826b
- Published
- 2026 Aug 5
- Container
- Physical chemistry chemical physics : PCCP
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1039/d6cp01826b,
title = {Machine learning force fields for inorganic crystalline materials: principles, advances, and emerging applications.},
author = {Yi J and Zhan Y and Hu Y and Zhao S and Li H},
year = {2026},
journal = {Physical chemistry chemical physics : PCCP},
doi = {10.1039/d6cp01826b},
url = {https://doi.org/10.1039/d6cp01826b}
}RIS
TY - JOUR TI - Machine learning force fields for inorganic crystalline materials: principles, advances, and emerging applications. AU - Yi J AU - Zhan Y AU - Hu Y AU - Zhao S AU - Li H PY - 2026 JO - Physical chemistry chemical physics : PCCP DO - 10.1039/d6cp01826b UR - https://doi.org/10.1039/d6cp01826b ER -
APA
J, Y., Y, Z., Y, H., S, Z., & H, L. (2026). Machine learning force fields for inorganic crystalline materials: principles, advances, and emerging applications.. Physical chemistry chemical physics : PCCP. https://doi.org/10.1039/d6cp01826b
Source records
- pubmed · retrieved 2026-09-26T13:47:28.355Z