Modeling high-entropy transition-metal alloys with alchemical compression: dataset HEA25

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<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>Lopanitsyna, Nataliya</dc:creator>
  <dc:creator>Fraux, Guillaume</dc:creator>
  <dc:creator>Springer, Maximilian A.</dc:creator>
  <dc:creator>De, Sandip</dc:creator>
  <dc:creator>Ceriotti, Michele</dc:creator>
  <dc:date>2023-04-05</dc:date>
  <dc:description>Alloys composed of several elements in roughly equimolar composition, often referred to as high-entropy alloys, have long been of interest for their thermodynamics and peculiar mechanical properties, and more recently for their potential application in catalysis. They are a considerable challenge to traditional atomistic modeling, and also to data-driven potentials that for the most part have memory footprint, computational effort and data requirements which scale poorly with the number of elements included. We apply a recently proposed scheme to compress chemical information in a lower-dimensional space, which reduces dramatically the cost of the model with negligible loss of accuracy, to build a potential that can describe 25 d-block transition metals. The model shows semi-quantitative accuracy for prototypical alloys and is remarkably stable when extrapolating to structures outside its training set. 
In this record, we provide a dataset containing 25,000 structures utilized for fitting the aforementioned potential, with a focus on 25 d-block transition metals, excluding Tc, Cd, Re, Os and Hg.</dc:description>
  <dc:identifier>https://archive.materialscloud.org/record/2023.57</dc:identifier>
  <dc:identifier>doi:10.24435/materialscloud:73-yn</dc:identifier>
  <dc:identifier>mcid:2023.57</dc:identifier>
  <dc:identifier>oai:materialscloud.org:1721</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>machine learning</dc:subject>
  <dc:subject>high-entropy alloys</dc:subject>
  <dc:subject>neural network potential</dc:subject>
  <dc:subject>alchemical compression</dc:subject>
  <dc:subject>MARVEL</dc:subject>
  <dc:title>Modeling high-entropy transition-metal alloys with alchemical compression: dataset HEA25</dc:title>
  <dc:type>Dataset</dc:type>
</oai_dc:dc>