(595i) Computer-Aided Design of Novel Materials with Desired Electronic and Physical Properties

Historically, materials discovery is driven by a laborious trial-and-error process. The growth of materials databases and emerging informatics approaches finally offer the opportunity to transform this practice into data- and knowledge-driven rational design—accelerating discovery of novel materials exhibiting desired properties.

By using data from the AFLOW repository for high-throughput ab-initio calculations, we have generated Quantitative Materials Structure-Property Relationship (QMSPR) models to predict three critical material properties, namely the metal/insulator classification, bulk modulus, Fermi energy, and band gap energy. To enable these calculation, we have developed novel materials descriptors such as universal property-labelled fragments (PLMF).[1] We have established that the accuracy of predictions obtained with machine learning models approaches 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 used materials informatics approach for solar energy conversion, specifically to find a novel photocathode material for dye-sensitized solar cells (DSSCs). By conducting a virtual screening of 50,000 known inorganic compounds, we have identified lead titanate (PbTiO₃), a perovskite, as the most promising photocathode material, far from the traditionally used base elements or crystal structures. Subsequently this finding was confirmed experimentally.

1. O. Isayev*, C. Oses, S. Curtarolo, A. Tropsha. Universal Fragment Descriptors for Predicting Electronic Properties of Inorganic Crystals. Nature Comm. 2017, Accepted. Preprint: https://arxiv.org/abs/1608.04782