Universal fragment descriptors for predicting properties of inorganic crystals

Olexandr Isayev (), Corey Oses, Cormac Toher, Eric Gossett, Stefano Curtarolo () and Alexander Tropsha
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Olexandr Isayev: Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina
Corey Oses: Center for Materials Genomics, Duke University
Cormac Toher: Center for Materials Genomics, Duke University
Eric Gossett: Center for Materials Genomics, Duke University
Stefano Curtarolo: Center for Materials Genomics, Duke University
Alexander Tropsha: Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina

Nature Communications, 2017, vol. 8, issue 1, 1-12

Abstract: Abstract 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.

Date: 2017
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DOI: 10.1038/ncomms15679

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