Molecular Conditional Generation and Property Analysis of Non-Fullerene Acceptors with Deep Learning.

Peng, Shi-Ping; Yang, Xin-Yu; Zhao, Yi

Peng, Shi-Ping; Yang, Xin-Yu; Zhao, Yi.

Afiliación

Peng SP; State Key Laboratory for Physical Chemistry of Solid Surfaces, Fujian Provincial Key Lab of Theoretical and Computational Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China.
Yang XY; State Key Laboratory for Physical Chemistry of Solid Surfaces, Fujian Provincial Key Lab of Theoretical and Computational Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China.
Zhao Y; State Key Laboratory for Physical Chemistry of Solid Surfaces, Fujian Provincial Key Lab of Theoretical and Computational Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China.

Int J Mol Sci ; 22(16)2021 Aug 23.

Article en En | MEDLINE | ID: mdl-34445805

Asunto(s)

Fulerenos/química; Aprendizaje Profundo; Diseño de Fármacos; Aprendizaje Automático; Modelos Moleculares; Estructura Molecular; Redes Neurales de la Computación; Reproducibilidad de los Resultados

Palabras clave

chemical space; convolutional neural networks; frontier molecular orbital energies; molecular generation model; non-fullerene acceptors

Texto completo

Imprimir

XML

PubMed Links

Buscar en Google

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Fulerenos Tipo de estudio: Prognostic_studies Idioma: En Revista: Int J Mol Sci Año: 2021 Tipo del documento: Article País de afiliación: China

Texto completo

Imprimir

XML

PubMed Links

Buscar en Google