DeFecT-FF: a machine learning force field framework for high throughput defect modeling in CdTe-based solar cells.

Rahman MH, Biswas M, Mannodi-Kanakkithodi A

DOI: 10.1039/d6cp00170j
Published: 2026 May 6
Container: Physical chemistry chemical physics : PCCP
Publisher: Not recorded
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1039/d6cp00170j,
  title = {DeFecT-FF: a machine learning force field framework for high throughput defect modeling in CdTe-based solar cells.},
  author = {Rahman MH and Biswas M and Mannodi-Kanakkithodi A},
  year = {2026},
  journal = {Physical chemistry chemical physics : PCCP},
  doi = {10.1039/d6cp00170j},
  url = {https://doi.org/10.1039/d6cp00170j}
}

RIS

TY  - JOUR
TI  - DeFecT-FF: a machine learning force field framework for high throughput defect modeling in CdTe-based solar cells.
AU  - Rahman MH
AU  - Biswas M
AU  - Mannodi-Kanakkithodi A
PY  - 2026
JO  - Physical chemistry chemical physics : PCCP
DO  - 10.1039/d6cp00170j
UR  - https://doi.org/10.1039/d6cp00170j
ER  -

APA

MH, R., M, B., & A, M. (2026). DeFecT-FF: a machine learning force field framework for high throughput defect modeling in CdTe-based solar cells.. Physical chemistry chemical physics : PCCP. https://doi.org/10.1039/d6cp00170j

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pubmed · retrieved 2026-09-26T03:17:12.753Z