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Volumn 92, Issue 9, 2015, Pages

Learning scheme to predict atomic forces and accelerate materials simulations

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EID: 84943744240     PISSN: 10980121     EISSN: 1550235X     Source Type: Journal    
DOI: 10.1103/PhysRevB.92.094306     Document Type: Article
Times cited : (216)

References (32)
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    • See Supplemental Material at http://link.aps.org/supplemental/10.1103/PhysRevB.92.094306 for (i) a video of geometry optimization of defects in bulk fcc Al, (ii) a video of a molecular dynamics simulation of adatom migration on the Al (111) surface, and (iii) a discussion on using the direction-resolved atomic fingerprint for multielemental systems, including some preliminary force predictions.
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    • pwtools", (unpublished)
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.