Efficiently Trained Deep Learning Potential for Graphane-Reference-Cited by-同舟云学术

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Efficiently Trained Deep Learning Potential for Graphane

Published:2021-07-01 Issue:27 Volume:125 Page:14874-14882
ISSN:1932-7447
Container-title:The Journal of Physical Chemistry C
language:en
Short-container-title:J. Phys. Chem. C

Author:

Achar Siddarth K.¹^ORCID,Zhang Linfeng²^ORCID,Johnson J. Karl³^ORCID

Affiliation:

1. Computational Modeling & Simulation Program, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States

2. Program in Applied and Computational Mathematics, Princeton University, Princeton, New Jersey 08544, United States

3. Department of Chemical & Petroleum Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States

Funder

Division of Chemical, Bioengineering, Environmental, and Transport Systems

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.1c01411

Reference58 articles.

1. Graphane: A two-dimensional hydrocarbon

2. Graphene to graphane: a theoretical study

3. The response of mechanical and electronic properties of graphane to the elastic strain

4. Elastic properties of hydrogenated graphene

5. High-pressure hydrogenation of graphene: towards graphane

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