Uncovering structure-property relationships of materials by subgroup discovery. (25th January 2017)

• Record Type: Journal Article
• Authors: Goldsmith, Bryan R; Boley, Mario; Vreeken, Jilles; Scheffler, Matthias; Ghiringhelli, Luca M
• Is Part Of: New journal of physics. Volume 19:Number 1(2017:Jan.)
• Publication Date: 2017-01-25
• Subjects: big-data analytics -- data mining -- pattern discovery -- machine learning -- octet binary semiconductors -- gold clusters; Physics -- Periodicals; Physics; Periodicals; 530.05
• DOI: 10.1088/1367-2630/aa57c2
• Languages: English
• ISSNs: 1367-2630

Abstract

Abstract: Subgroup discovery (SGD) is presented here as a data-mining approach to help find interpretable local patterns, correlations, and descriptors of a target property in materials-science data. Specifically, we will be concerned with data generated by density-functional theory calculations. At first, we demonstrate that SGD can identify physically meaningful models that classify the crystal structures of 82 octet binary (OB) semiconductors as either rocksalt or zincblende. SGD identifies an interpretable two-dimensional model derived from only the atomic radii of valence s and p orbitals that properly classifies the crystal structures for 79 of the 82 OB semiconductors. The SGD framework is subsequently applied to 24 400 configurations of neutral gas-phase gold clusters with 5–14 atoms to discern general patterns between geometrical and physicochemical properties. For example, SGD helps find that van der Waals interactions within gold clusters are linearly correlated with their radius of gyration and are weaker for planar clusters than for nonplanar clusters. Also, a descriptor that predicts a local linear correlation between the chemical hardness and the cluster isomer stability is found for the even-sized gold clusters.