Machine Learning Methods to Predict Density Functional Theory B3LYP Energies of HOMO and LUMO Orbitals-Reference-Cited by-同舟云学术

Machine Learning Methods to Predict Density Functional Theory B3LYP Energies of HOMO and LUMO Orbitals

Published:2016-12-29 Issue:1 Volume:57 Page:11-21
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Pereira Florbela¹,Xiao Kaixia²,Latino Diogo A. R. S.¹,Wu Chengcheng²,Zhang Qingyou²,Aires-de-Sousa Joao¹

Affiliation:

1. LAQV and REQUIMTE, Departamento de Química, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal

2. Henan Engineering Research Center of Industrial Circulating Water Treatment, College of Chemistry and Chemical Engineering, Henan University, Kaifeng, 475004, PR China

Funder

Ministry of Education of the People's Republic of China

National Natural Science Foundation of China

Funda??o para a Ci?ncia e a Tecnologia

International Science and Technology Cooperation of Henan Province

European Regional Development Fund

Publisher

American Chemical Society (ACS)

Subject

Library and Information Sciences,Computer Science Applications,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.6b00340

Reference63 articles.

1. Learning from the Harvard Clean Energy Project: The Use of Neural Networks to Accelerate Materials Discovery

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3. Design, synthesis and photophysical studies of dipyrromethene-based materials: insights into their applications in organic photovoltaic devices

4. Material and device considerations for organic thin-film transistor sensors

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