Finding new crystalline compounds using chemical similarity

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<oai_dc:dc xmlns:dc="" xmlns:oai_dc="" xmlns:xsi="" xsi:schemaLocation="">
  <dc:creator>Wang, Hai-Chen</dc:creator>
  <dc:creator>Botti, Silvana</dc:creator>
  <dc:creator>L. Marques, Miguel A.</dc:creator>
  <dc:description>We proposed an efficient high-throughput scheme for the discovery of new stable crystalline phases. Our approach was based on the transmutation of known compounds, through the substitution of atoms in the crystal structure with chemically similar ones. The concept of similarity is defined quantitatively using a measure of chemical replaceability, extracted by data mining experimental databases. In this way we build more than 250k possible crystal phases, with almost 20k that are on the convex hull of stability. This dataset contains the optimized structure and the energy of these 250k materials calculated with the PBE approximation, in a format that is convenient for data-mining or for machine-learning applications.</dc:description>
  <dc:publisher>Materials Cloud</dc:publisher>
  <dc:rights>Creative Commons Attribution 4.0 International</dc:rights>
  <dc:subject>density-functional theory</dc:subject>
  <dc:subject>machine learning</dc:subject>
  <dc:title>Finding new crystalline compounds using chemical similarity</dc:title>