Improving the Performance of Long-Range-Corrected Exchange-Correlation Functional with an Embedded Neural Network-Reference-Cited by-同舟云学术

Improving the Performance of Long-Range-Corrected Exchange-Correlation Functional with an Embedded Neural Network

Published:2017-09-19 Issue:38 Volume:121 Page:7273-7281
ISSN:1089-5639
Container-title:The Journal of Physical Chemistry A
language:en
Short-container-title:J. Phys. Chem. A

Author:

Liu Qin¹,Wang JingChun¹,Du PengLi¹,Hu LiHong²,Zheng Xiao¹^ORCID,Chen GuanHua³

Affiliation:

1. Hefei National Laboratory for Physical Sciences at the Microscale & Synergetic Innovation Center of Quantum Information and Quantum Physics, University of Science and Technology of China, Hefei, Anhui 230026, China

2. School of Computer Science and Information Technology, Northeast Normal University, Changchun, Jilin 130000, China

3. Department of Chemistry, The University of Hong Kong, Pokfulam Road, Hong Kong, China

Funder

Ministry of Education of the People's Republic of China

Ministry of Science and Technology of the People's Republic of China

National Natural Science Foundation of China

Publisher

American Chemical Society (ACS)

Subject

Physical and Theoretical Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpca.7b07045

Reference100 articles.

1. Genetics-based machine learning and behavior-based robotics: a new synthesis

2. Kalchbrenner, N.; Blunsom, P.Recurrent Continuous Translation Models. InProceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, Seattle, Washington, 18–21 October, 2013;Association for Computational Linguistics:Stroudsburg, PA, 2013; pp1700–1709

3. Bahdanau, D.; Cho, K.; Bengio, Y.Neural Machine Translation by Jointly Learning to Align and Translate.arXiv:1409.0473, 2014.

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