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Machine-learning interatomic potentials enable first-principles multiscale modeling of lattice thermal conductivity in graphene/borophene heterostructures

Crossref DOI link: https://doi.org/10.1039/d0mh00787k

Published Online: 2020

Update policy: https://doi.org/10.1039/rsc_crossmark_policy

Authors

Mortazavi, Bohayra https://orcid.org/0000-0003-3031-5057
Podryabinkin, Evgeny V.

Roche, Stephan

Rabczuk, Timon

Zhuang, Xiaoying

Shapeev, Alexander V.
Funding

Funding for this research was provided by:

Deutsche Forschungsgemeinschaft (390833453)

Russian Science Foundation (18-13-00479)

Ministerio de Economía y Competitividad (SEV-2017- 0706)

Generalitat de Catalunya
License Information

Accepted Manuscript valid from 2021-06-08
More Information

SIMILARITY CHECK

: This document is Similarity Check deposited

RELATED DATA

: Supplementary Information

IDENTIFIERS

: Bohayra Mortazavi (ORCID)

PEER REVIEW METHOD

: Single-blind

PUBLICATION HISTORY

: Received 12 May 2020; Accepted 8 June 2020; Accepted Manuscript published 8 June 2020; Advance Article published 18 June 2020; Version of Record published 1 September 2020

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