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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
Fonte: Journal of Chemical Information and Modeling. Unidade: IQSC
Assuntos: 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: 14 jun. 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 jun. 14 ] 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 jun. 14 ] Available from: https://doi.org/10.1021/acs.jcim.0c01267
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
Fonte: 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: 14 jun. 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 jun. 14 ] 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 jun. 14 ] Available from: https://doi.org/10.1021/acs.jpca.0c05969
Artigo de periodico
Ab initio insights into the structural, energetic, electronic, and stability properties of mixed CenZr15 nO30 nanoclusters (2019)
Felício-Sousa, Priscilla ; Mucelini, Johnatan ; Besse, Larissa Zibordi ; Andriani, Karla Furtado ; Seminovski, Yohanna; Prati, Ronaldo Cristiano ; Silva, Juarez Lopes Ferreira da
Fonte: Physical Chemistry Chemical Physics. Unidade: IQSC
Assuntos: FÍSICO-QUÍMICA, NANOPARTÍCULAS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
FELÍCIO-SOUSA, Priscilla et al. Ab initio insights into the structural, energetic, electronic, and stability properties of mixed CenZr15 nO30 nanoclusters. Physical Chemistry Chemical Physics, v. 21, p. 26637-26646, 2019Tradução . . Disponível em: https://doi-org.ez67.periodicos.capes.gov.br/10.1039/C9CP04762J. Acesso em: 14 jun. 2024.
APA
Felício-Sousa, P., Mucelini, J., Besse, L. Z., Andriani, K. F., Seminovski, Y., Prati, R. C., & Silva, J. L. F. da. (2019). Ab initio insights into the structural, energetic, electronic, and stability properties of mixed CenZr15 nO30 nanoclusters. Physical Chemistry Chemical Physics, 21, 26637-26646. doi:10.1039/c9cp04762j
NLM
Felício-Sousa P, Mucelini J, Besse LZ, Andriani KF, Seminovski Y, Prati RC, Silva JLF da. Ab initio insights into the structural, energetic, electronic, and stability properties of mixed CenZr15 nO30 nanoclusters [Internet]. Physical Chemistry Chemical Physics. 2019 ; 21 26637-26646.[citado 2024 jun. 14 ] Available from: https://doi-org.ez67.periodicos.capes.gov.br/10.1039/C9CP04762J
Vancouver
Felício-Sousa P, Mucelini J, Besse LZ, Andriani KF, Seminovski Y, Prati RC, Silva JLF da. Ab initio insights into the structural, energetic, electronic, and stability properties of mixed CenZr15 nO30 nanoclusters [Internet]. Physical Chemistry Chemical Physics. 2019 ; 21 26637-26646.[citado 2024 jun. 14 ] Available from: https://doi-org.ez67.periodicos.capes.gov.br/10.1039/C9CP04762J

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