Active Learning in Bayesian Neural Networks for Bandgap Predictions of Novel Van der Waals Heterostructures-Reference-Cited by-同舟云学术

Active Learning in Bayesian Neural Networks for Bandgap Predictions of Novel Van der Waals Heterostructures

Published:2021-08-22 Issue:11 Volume:3 Page:2100080
ISSN:2640-4567
Container-title:Advanced Intelligent Systems
language:en
Short-container-title:Advanced Intelligent Systems

Author:

Fronzi Marco¹²,Isayev Olexandr³,Winkler David A.⁴⁵⁶^ORCID,Shapter Joseph G.⁷⁸,Ellis Amanda V.⁹,Sherrell Peter C.⁹,Shepelin Nick A.⁹¹⁰,Corletto Alexander⁷,Ford Michael J.²

Affiliation:

1. SIT Research Laboratories Shibaura Institute of Technology 3-7-5, Toyosu, Koto-ku Tokyo 135-8548 Japan

2. School of Mathematical and Physical Science University of Technology Sydney Ultimo NSW 2007 Australia

3. Department of Chemistry Mellon College of Science Carnegie Mellon University Pittsburgh PA 15219 USA

4. Monash Institute of Pharmaceutical Sciences Monash University 381 Royal Parade Parkville VIC 3052 Australia

5. Department of Biochemistry and Genetics La Trobe Institute for Molecular Science La Trobe University Kingsbury Drive Bundoora VIC 3086 Australia

6. School of Pharmacy The University of Nottingham Nottingham NG7 2RD UK

7. Australian Institute for Bioengineering and Nanotechnology The University of Queensland St Lucia Brisbane Qld 4072 Australia

8. College of Science and Engineering Flinders University Bedford Park Adelaide SA 5042 Australia

9. Department of Chemical Engineering University of Melbourne Parkville VIC 3010 Australia

10. Laboratory for Multiscale Materials Experiments Paul Scherrer Institut Villigen CH-5232 Switzerland

Publisher

Wiley

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/aisy.202100080

Reference60 articles.

1. Biased Bilayer Graphene: Semiconductor with a Gap Tunable by the Electric Field Effect

2. Strain-engineered artificial atom as a broad-spectrum solar energy funnel

3. The electronic properties of graphene

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5. Bandgap engineering of two-dimensional semiconductor materials

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