<?xml version='1.0' encoding='utf-8'?> <oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"> <dc:creator>Halim, Harry Handoko</dc:creator> <dc:creator>Morikawa, Yoshitada</dc:creator> <dc:date>2022-06-15</dc:date> <dc:description>The mapped gaussian process (MGP) force-field used to elucidate the surface alloying of Cu-Zn. The force-field is made based on first-principles data by using machine-learning technique called Gaussian Process as implemented in FLARE package (https://github.com/mir-group/flare). Active and on-the-fly learning were employed to build the database efficiently. The simulation reveals atomistic details of the alloying process, i.e., the incorporation of deposited Zn adatoms to the Cu substrate. The surface alloying is found to start at upper and lower terraces near the step edge, which emphasize the role of steps and kinks in the alloying. The incorporation of Zn at the middle terrace was found at the later stage of the simulation.</dc:description> <dc:identifier>https://staging-archive.materialscloud.org/record/2022.79</dc:identifier> <dc:identifier>doi:10.24435/materialscloud:gh-wt</dc:identifier> <dc:identifier>mcid:2022.79</dc:identifier> <dc:identifier>oai:materialscloud.org:1385</dc:identifier> <dc:language>en</dc:language> <dc:publisher>Materials Cloud</dc:publisher> <dc:rights>info:eu-repo/semantics/openAccess</dc:rights> <dc:rights>Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights> <dc:subject>Cu-Zn surface</dc:subject> <dc:subject>machine learning</dc:subject> <dc:subject>molecular dynamics</dc:subject> <dc:subject>first principles</dc:subject> <dc:title>The mapped gaussian process (MGP) force-field of Cu-Zn surface alloy</dc:title> <dc:type>Dataset</dc:type> </oai_dc:dc>