Quantum chemistry–machine learning approach for predicting and elucidating molecular hyperpolarizability: Application to [2.2]paracyclophane-containing push

Quantum chemistry–machine learning approach for predicting and elucidating molecular hyperpolarizability: Application to [2.2]paracyclophane-containing push–pull polymers

Published:2021-03-28 Issue:12 Volume:154 Page:124107
ISSN:0021-9606
Container-title:The Journal of Chemical Physics
language:en
Short-container-title:J. Chem. Phys.

Author:

Ivonina Mariia V.¹^ORCID,Orimoto Yuuichi²^ORCID,Aoki Yuriko²^ORCID

Affiliation:

1. Department of Molecular and Material Sciences, Interdisciplinary Graduate School of Engineering Sciences, Kyushu University, Kasuga, Fukuoka 816-8580, Japan

2. Department of Material Sciences, Faculty of Engineering Sciences, Kyushu University, Kasuga, Fukuoka 816-8580, Japan

Funder

Japan Society for the Promotion of Science

Core Research for Evolutional Science and Technology

Publisher

AIP Publishing

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

https://aip.scitation.org/doi/pdf/10.1063/5.0040342

Reference49 articles.

1. Optical nonlinearities of conjugated molecules. Stilbene derivatives and highly polar aromatic compounds