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Title: Accurate interatomic force fields via machine learning with covariant kernels

Authors:
; ;
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1372519
Grant/Contract Number:  
AC02-06CH11357
Resource Type:
Journal Article: Publisher's Accepted Manuscript
Journal Name:
Physical Review B
Additional Journal Information:
Journal Name: Physical Review B Journal Volume: 95 Journal Issue: 21; Journal ID: ISSN 2469-9950
Publisher:
American Physical Society
Country of Publication:
United States
Language:
English

Citation Formats

Glielmo, Aldo, Sollich, Peter, and De Vita, Alessandro. Accurate interatomic force fields via machine learning with covariant kernels. United States: N. p., 2017. Web. doi:10.1103/PhysRevB.95.214302.
Glielmo, Aldo, Sollich, Peter, & De Vita, Alessandro. Accurate interatomic force fields via machine learning with covariant kernels. United States. doi:10.1103/PhysRevB.95.214302.
Glielmo, Aldo, Sollich, Peter, and De Vita, Alessandro. Thu . "Accurate interatomic force fields via machine learning with covariant kernels". United States. doi:10.1103/PhysRevB.95.214302.
@article{osti_1372519,
title = {Accurate interatomic force fields via machine learning with covariant kernels},
author = {Glielmo, Aldo and Sollich, Peter and De Vita, Alessandro},
abstractNote = {},
doi = {10.1103/PhysRevB.95.214302},
journal = {Physical Review B},
number = 21,
volume = 95,
place = {United States},
year = {Thu Jun 08 00:00:00 EDT 2017},
month = {Thu Jun 08 00:00:00 EDT 2017}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record at 10.1103/PhysRevB.95.214302

Citation Metrics:
Cited by: 15 works
Citation information provided by
Web of Science

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Works referenced in this record:

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Cramming More Components Onto Integrated Circuits
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Self-consistent-charge density-functional tight-binding method for simulations of complex materials properties
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