Universal fragment descriptors for predicting properties of inorganic crystals.

Journal Article (Journal Article)

Although historically materials discovery has been driven by a laborious trial-and-error process, knowledge-driven materials design can now be enabled by the rational combination of Machine Learning methods and materials databases. Here, data from the AFLOW repository for ab initio calculations is combined with Quantitative Materials Structure-Property Relationship models to predict important properties: metal/insulator classification, band gap energy, bulk/shear moduli, Debye temperature and heat capacities. The prediction's accuracy compares well with the quality of the training data for virtually any stoichiometric inorganic crystalline material, reciprocating the available thermomechanical experimental data. The universality of the approach is attributed to the construction of the descriptors: Property-Labelled Materials Fragments. The representations require only minimal structural input allowing straightforward implementations of simple heuristic design rules.

Full Text

Duke Authors

Cited Authors

  • Isayev, O; Oses, C; Toher, C; Gossett, E; Curtarolo, S; Tropsha, A

Published Date

  • June 5, 2017

Published In

Volume / Issue

  • 8 /

Start / End Page

  • 15679 -

PubMed ID

  • 28580961

Pubmed Central ID

  • PMC5465371

Electronic International Standard Serial Number (EISSN)

  • 2041-1723

International Standard Serial Number (ISSN)

  • 2041-1723

Digital Object Identifier (DOI)

  • 10.1038/ncomms15679


  • eng