QMLMaterial─A Quantum Machine Learning Software for Material Design and Discovery

Maicon Pierre Lourenço, Lizandra Barrios Herrera, Jiří Hostaš, Patrizia Calaminici, Andreas M. Köster, Alain Tchagang, Dennis R. Salahub

Open source

DOI: 10.1021/acs.jctc.3c00566
Published: 2023-08-15
Container: Journal of Chemical Theory and Computation
Publisher: American Chemical Society (ACS)
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1021/acs.jctc.3c00566,
  title = {QMLMaterial─A Quantum Machine Learning Software for Material Design and Discovery},
  author = {Maicon Pierre Lourenço and Lizandra Barrios Herrera and Jiří Hostaš and Patrizia Calaminici and Andreas M. Köster and Alain Tchagang and Dennis R. Salahub},
  year = {2023},
  journal = {Journal of Chemical Theory and Computation},
  doi = {10.1021/acs.jctc.3c00566},
  url = {https://doi.org/10.1021/acs.jctc.3c00566}
}

RIS

TY  - JOUR
TI  - QMLMaterial─A Quantum Machine Learning Software for Material Design and Discovery
AU  - Maicon Pierre Lourenço
AU  - Lizandra Barrios Herrera
AU  - Jiří Hostaš
AU  - Patrizia Calaminici
AU  - Andreas M. Köster
AU  - Alain Tchagang
AU  - Dennis R. Salahub
PY  - 2023
JO  - Journal of Chemical Theory and Computation
DO  - 10.1021/acs.jctc.3c00566
UR  - https://doi.org/10.1021/acs.jctc.3c00566
ER  -

APA

Lourenço, M. P., Herrera, L. B., Hostaš, J., Calaminici, P., Köster, A. M., Tchagang, A., & Salahub, D. R. (2023). QMLMaterial─A Quantum Machine Learning Software for Material Design and Discovery. Journal of Chemical Theory and Computation. https://doi.org/10.1021/acs.jctc.3c00566

Source records

crossref · retrieved 2026-09-26T08:49:53.516Z