Machine Learning with Multilevel Descriptors for Screening of Inorganic Nonlinear Optical Crystals-Reference-Cited by-同舟云学术

Machine Learning with Multilevel Descriptors for Screening of Inorganic Nonlinear Optical Crystals

Published:2021-11-08 Issue:45 Volume:125 Page:25175-25188
ISSN:1932-7447
Container-title:The Journal of Physical Chemistry C
language:en
Short-container-title:J. Phys. Chem. C

Author:

Zhang Zhan-Yun¹,Liu Xin¹,Shen Lin¹²^ORCID,Chen Ling¹^ORCID,Fang Wei-Hai¹²^ORCID

Affiliation:

1. Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education, College of Chemistry, Beijing Normal University, Beijing 100875, P. R. China

2. Yantai-Jingshi Institute of Material Genome Engineering, 48 Nanchang Road, Yantai 265505, Shandong, P. R. China

Funder

National Natural Science Foundation of China

Publisher

American Chemical Society (ACS)

Subject

Surfaces, Coatings and Films,Physical and Theoretical Chemistry,General Energy,Electronic, Optical and Magnetic Materials

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpcc.1c06049

Reference54 articles.

1. Second-order nonlinear optical crystals with mixed anions

2. Nitrate nonlinear optical crystals: A survey on structure-performance relationships

3. First-Principles High-Throughput Screening Pipeline for Nonlinear Optical Materials: Application to Borates

4. Lead Mixed Oxyhalides Satisfying All Fundamental Requirements for High‐Performance Mid‐Infrared Nonlinear Optical Materials

5. Structure-property survey and computer-assisted screening of mid-infrared nonlinear optical chalcohalides

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