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Artigo de periodico
Correlation-based framework for extraction of insights from quantum chemistry databases: Applications for nanoclusters (2021)
Mucelini, Johnatan ; Quiles, Marcos Gonçalves ; Prati, Ronaldo Cristiano ; Silva, Juarez Lopes Ferreira da
Source: Journal of Chemical Information and Modeling. Unidade: IQSC
Subjects: QUÍMICA QUÂNTICA, MINERAÇÃO DE DADOS, FRAMEWORKS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
MUCELINI, Johnatan et al. Correlation-based framework for extraction of insights from quantum chemistry databases: Applications for nanoclusters. Journal of Chemical Information and Modeling, v. 61, p. 1125-1135, 2021Tradução . . Disponível em: https://doi.org/10.1021/acs.jcim.0c01267. Acesso em: 01 nov. 2024.
APA
Mucelini, J., Quiles, M. G., Prati, R. C., & Silva, J. L. F. da. (2021). Correlation-based framework for extraction of insights from quantum chemistry databases: Applications for nanoclusters. Journal of Chemical Information and Modeling, 61, 1125-1135. doi:10.1021/acs.jcim.0c01267
NLM
Mucelini J, Quiles MG, Prati RC, Silva JLF da. Correlation-based framework for extraction of insights from quantum chemistry databases: Applications for nanoclusters [Internet]. Journal of Chemical Information and Modeling. 2021 ; 61 1125-1135.[citado 2024 nov. 01 ] Available from: https://doi.org/10.1021/acs.jcim.0c01267
Vancouver
Mucelini J, Quiles MG, Prati RC, Silva JLF da. Correlation-based framework for extraction of insights from quantum chemistry databases: Applications for nanoclusters [Internet]. Journal of Chemical Information and Modeling. 2021 ; 61 1125-1135.[citado 2024 nov. 01 ] Available from: https://doi.org/10.1021/acs.jcim.0c01267
Artigo de periodico
Ab initio insights into the formation mechanisms of 55-atom Pt- based core−Shell Nanoalloys (2020)
Mendes, Paulo de Carvalho Dias ; Justo, Stella Granatto ; Mucelini, Johnatan ; Soares, Marinalva Dias ; Batista, Krys Elly de Araújo ; Quiles, Marcos Gonçalves ; Piotrowski, Maurício Jeomar ; Silva, Juarez Lopes Ferreira da
Source: The Journal of Physical Chemistry C. Unidade: IQSC
Subjects: CATALISADORES, NANOPARTÍCULAS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
MENDES, Paulo de Carvalho Dias et al. Ab initio insights into the formation mechanisms of 55-atom Pt- based core−Shell Nanoalloys. The Journal of Physical Chemistry C, v. 124, n. 1, p. 1158-1164, 2020Tradução . . Disponível em: https://doi.org/10.1021/acs.jpcc.9b09561. Acesso em: 01 nov. 2024.
APA
Mendes, P. de C. D., Justo, S. G., Mucelini, J., Soares, M. D., Batista, K. E. de A., Quiles, M. G., et al. (2020). Ab initio insights into the formation mechanisms of 55-atom Pt- based core−Shell Nanoalloys. The Journal of Physical Chemistry C, 124( 1), 1158-1164. doi:10.1021/acs.jpcc.9b09561
NLM
Mendes P de CD, Justo SG, Mucelini J, Soares MD, Batista KE de A, Quiles MG, Piotrowski MJ, Silva JLF da. Ab initio insights into the formation mechanisms of 55-atom Pt- based core−Shell Nanoalloys [Internet]. The Journal of Physical Chemistry C. 2020 ; 124( 1): 1158-1164.[citado 2024 nov. 01 ] Available from: https://doi.org/10.1021/acs.jpcc.9b09561
Vancouver
Mendes P de CD, Justo SG, Mucelini J, Soares MD, Batista KE de A, Quiles MG, Piotrowski MJ, Silva JLF da. Ab initio insights into the formation mechanisms of 55-atom Pt- based core−Shell Nanoalloys [Internet]. The Journal of Physical Chemistry C. 2020 ; 124( 1): 1158-1164.[citado 2024 nov. 01 ] Available from: https://doi.org/10.1021/acs.jpcc.9b09561
Artigo de periodico
Machine Learning Prediction of Nine Molecular Properties Based on the SMILES Representation of the QM9 Quantum-Chemistry Dataset (2020)
Pinheiro, Gabriel A.; Mucelini, Johnatan ; Soares, Marinalva Dias ; Prati, Ronaldo Cristiano ; Silva, Juarez Lopes Ferreira da ; Quiles, Marcos Gonçalves
Source: Journal of Physical Chemistry A. Unidades: IQSC, ICMC
Assunto: QUÍMICA QUÂNTICA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
PINHEIRO, Gabriel A. et al. Machine Learning Prediction of Nine Molecular Properties Based on the SMILES Representation of the QM9 Quantum-Chemistry Dataset. Journal of Physical Chemistry A, v. No 2020, n. 47, p. 9854–9866, 2020Tradução . . Disponível em: https://doi.org/10.1021/acs.jpca.0c05969. Acesso em: 01 nov. 2024.
APA
Pinheiro, G. A., Mucelini, J., Soares, M. D., Prati, R. C., Silva, J. L. F. da, & Quiles, M. G. (2020). Machine Learning Prediction of Nine Molecular Properties Based on the SMILES Representation of the QM9 Quantum-Chemistry Dataset. Journal of Physical Chemistry A, No 2020( 47), 9854–9866. doi:10.1021/acs.jpca.0c05969
NLM
Pinheiro GA, Mucelini J, Soares MD, Prati RC, Silva JLF da, Quiles MG. Machine Learning Prediction of Nine Molecular Properties Based on the SMILES Representation of the QM9 Quantum-Chemistry Dataset [Internet]. Journal of Physical Chemistry A. 2020 ; No 2020( 47): 9854–9866.[citado 2024 nov. 01 ] Available from: https://doi.org/10.1021/acs.jpca.0c05969
Vancouver
Pinheiro GA, Mucelini J, Soares MD, Prati RC, Silva JLF da, Quiles MG. Machine Learning Prediction of Nine Molecular Properties Based on the SMILES Representation of the QM9 Quantum-Chemistry Dataset [Internet]. Journal of Physical Chemistry A. 2020 ; No 2020( 47): 9854–9866.[citado 2024 nov. 01 ] Available from: https://doi.org/10.1021/acs.jpca.0c05969

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