Data mining for materials: Computational experiments with AB compounds

Yousef Saad, Da Gao, Thanh Ngo, Scotty Bobbitt, James R. Chelikowsky, Wanda Andreoni

Research output: Contribution to journalArticle

63 Scopus citations

Abstract

Machine learning is a broad discipline that comprises a variety of techniques for extracting meaningful information and patterns from data. It draws on knowledge and "know-how" from various scientific areas such as statistics, graph theory, linear algebra, databases, mathematics, and computer science. Recently, materials scientists have begun to explore data mining ideas for discovery in materials. In this paper we explore the power of these methods for studying binary compounds that are well characterized and are often used as a test bed. By mining properties of the constituent atoms, three materials research relevant tasks, namely, separation of a number of compounds into subsets in terms of their crystal structure, grouping of an unknown compound into the most characteristically similar peers (in one instance, 100% accuracy is achieved), and specific property prediction (the melting point), are explored.

Original languageEnglish (US)
Article number104104
JournalPhysical Review B - Condensed Matter and Materials Physics
Volume85
Issue number10
DOIs
StatePublished - Mar 6 2012

Fingerprint Dive into the research topics of 'Data mining for materials: Computational experiments with AB compounds'. Together they form a unique fingerprint.

  • Cite this