Quantifying Base-Dependent Lewis Acidity in Substituted Boranes through DFT and Machine Learning

Amir Mahdian, Kari Laasonen, Kaveh Farshadfar

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DOI: 10.1021/acs.inorgchem.6c04676
Published: 2026-10-19
Container: Inorganic Chemistry
Publisher: American Chemical Society (ACS)
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1021/acs.inorgchem.6c04676,
  title = {Quantifying
Base-Dependent Lewis Acidity in Substituted
Boranes through DFT and Machine Learning},
  author = {Amir Mahdian and Kari Laasonen and Kaveh Farshadfar},
  year = {2026},
  journal = {Inorganic Chemistry},
  doi = {10.1021/acs.inorgchem.6c04676},
  url = {https://doi.org/10.1021/acs.inorgchem.6c04676}
}

RIS

TY  - JOUR
TI  - Quantifying
Base-Dependent Lewis Acidity in Substituted
Boranes through DFT and Machine Learning
AU  - Amir Mahdian
AU  - Kari Laasonen
AU  - Kaveh Farshadfar
PY  - 2026
JO  - Inorganic Chemistry
DO  - 10.1021/acs.inorgchem.6c04676
UR  - https://doi.org/10.1021/acs.inorgchem.6c04676
ER  -

APA

Mahdian, A., Laasonen, K., & Farshadfar, K. (2026). Quantifying Base-Dependent Lewis Acidity in Substituted Boranes through DFT and Machine Learning. Inorganic Chemistry. https://doi.org/10.1021/acs.inorgchem.6c04676

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crossref · retrieved 2026-11-05T12:51:43.313Z