Pinheiro, Gabriel A 
; Calderan, Felipe V ; Silva, Juarez Lopes Ferreira da ; Quiles, Marcos Gonçalves
Source: Book of Abstracts. Conference titles: IEEE International Conference on Machine Learning and Applications (ICMLA). Unidade: IQSC
Subjects: ESTRUTURA MOLECULAR (QUÍMICA TEÓRICA), REDES NEURAIS, ALGORITMOS
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ABNT
PINHEIRO, Gabriel A et al. The impact of low-cost molecular geometry optimization in property prediction via graph neural network. 2022, Anais.. Nassau: Instituto de Química de São Carlos, Universidade de São Paulo, 2022. p. 603-608. Disponível em: https://doi.org/10.1109/ICMLA55696.2022.00092. Acesso em: 06 jul. 2024.APA
Pinheiro, G. A., Calderan, F. V., Silva, J. L. F. da, & Quiles, M. G. (2022). The impact of low-cost molecular geometry optimization in property prediction via graph neural network. In Book of Abstracts (p. 603-608). Nassau: Instituto de Química de São Carlos, Universidade de São Paulo. doi:10.1109/ICMLA55696.2022.00092NLM
Pinheiro GA, Calderan FV, Silva JLF da, Quiles MG. The impact of low-cost molecular geometry optimization in property prediction via graph neural network [Internet]. Book of Abstracts. 2022 ; 603-608.[citado 2024 jul. 06 ] Available from: https://doi.org/10.1109/ICMLA55696.2022.00092Vancouver
Pinheiro GA, Calderan FV, Silva JLF da, Quiles MG. The impact of low-cost molecular geometry optimization in property prediction via graph neural network [Internet]. Book of Abstracts. 2022 ; 603-608.[citado 2024 jul. 06 ] Available from: https://doi.org/10.1109/ICMLA55696.2022.00092
