A new MaterialGo database and its comparison with other high-throughput electronic structure databases for their predicted energy band gaps-Reference-Cited by-同舟云学术

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A new MaterialGo database and its comparison with other high-throughput electronic structure databases for their predicted energy band gaps

Published:2019-06-05 Issue:8 Volume:62 Page:1423-1430
ISSN:1674-7321
Container-title:Science China Technological Sciences
language:en
Short-container-title:Sci. China Technol. Sci.

Author:

Jie JianShu,Weng MouYi,Li ShunNing,Chen Dong,Li ShuCheng,Xiao WeiJi,Zheng JiaXin,Pan Feng,Wang LinWang

Publisher

Springer Science and Business Media LLC

Subject

General Engineering,General Materials Science

Link

http://link.springer.com/content/pdf/10.1007/s11431-019-9514-5.pdf

Reference36 articles.

1. Pilania G, Gubernatis J E, Lookman T. Multi-fidelity machine learning models for accurate bandgap predictions of solids. Comput Mater Sci, 2017, 129: 156–163

2. de Jong M, Chen W, Notestine R, et al. A statistical learning framework for materials science: Application to elastic moduli of k-nary inorganic Polycrystalline Compounds. Sci Rep, 2016, 6: 34256

3. Morales-García Á, Valero R, Illas F. An empirical, yet practical way to predict the band gap in solids by using density functional band structure calculations. J Phys Chem C, 2017, 121: 18862–18866

4. Belsky A, Hellenbrandt M, Karen V L, et al. New developments in the inorganic crystal structure database (ICSD): Accessibility in support of materials research and design. Acta Cryst Sect A Found Cryst, 2002, 58: 364–369

5. Bergerhoff G, Hundt R, Sievers R, et al. The inorganic crystal structure data base. J Chem Inf Model, 1983, 23: 66–69

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