Developing Efficient Small Molecule Acceptors with sp<sup>2</sup> -Hybridized Nitrogen at Different Positions by Density Functional Theory Calculations, Molecular Dynamics Simulations and Machine Learning.

Mahmood, Asif; Irfan, Ahmad; Wang, Jin-Liang

Developing Efficient Small Molecule Acceptors with sp² -Hybridized Nitrogen at Different Positions by Density Functional Theory Calculations, Molecular Dynamics Simulations and Machine Learning.

Mahmood, Asif; Irfan, Ahmad; Wang, Jin-Liang.

Afiliação

Mahmood A; Department Key Laboratory of Cluster Science of Ministry of Education, Beijing Key Laboratory of Photoelectronic/Electrophotonic Conversion Materials, School of Chemistry and Chemical Engineering, Beijing Institute of Technology, Beijing, 100081, P. R. China.
Irfan A; Department of Chemistry, College of Science, King Khalid University, Abha, 61413, Saudi Arabia.
Wang JL; Department Key Laboratory of Cluster Science of Ministry of Education, Beijing Key Laboratory of Photoelectronic/Electrophotonic Conversion Materials, School of Chemistry and Chemical Engineering, Beijing Institute of Technology, Beijing, 100081, P. R. China.

Chemistry ; 28(2): e202103712, 2022 Jan 10.

Article em En | MEDLINE | ID: mdl-34767281

Palavras-chave

Florgy-Huggins parameter; machine learning; molecular dynamics simulations; nitrogen; reorganization energy

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article