Historically, materials discovery has been driven by a laborious trial-and-error process. The growth of materials databases and emerging informatics approaches offer the opportunity to transform this practice into data- and knowledge-driven rational design—accelerating the discovery of novel materials with the desired properties. Using data from the AFLOW repository of high-throughput ab-initio calculations for inorganic molecules, we have generated Quantitative Materials Structure-Property Relationship (QMSPR) models to predict three critical material properties, namely metal/insulator classification, bulk modulus, Fermi energy, and band gap energy. To enable these calculations, we have developed novel materials descriptors such as universal property-labelled fragments (PLMF). We have established that the accuracy of predictions obtained with machine learning models matches that of GGA DFT functionals yet model development requires a minute fraction of computational time as compared to ab initio calculations. Notably, due to the representation of materials with PLMF, the QMSPR models are broadly applicable to virtually any stoichiometric inorganic materials. This representation also affords straightforward model interpretation in terms of simple heuristic design rules that could guide rational design of novel materials. As a proof-of-concept study we have employed a materials informatics approach to identify a novel photocathode material for dye-sensitized solar cells (DSSCs). By conducting a virtual screening of 50,000 known inorganic compounds, we have selected lead titanate (PbTiO3), a perovskite, as the most promising photocathode material, which differs substantially from the traditionally used base elements or crystal structures. Subsequent investigations have provided firm experimental support for this hypothesis.

Computer-aided design of novel materials with desired electronic and physical properties

253rd National Meeting (2017)

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