Open AccessDiscovery of complex oxides via automated experiments and data science
Author(s) -
Lusann Yang,
Joel A. Haber,
Zan Armstrong,
Samuel Yang,
Kevin Kan,
Lan Zhou,
Matthias H. Richter,
Chris Roat,
Nicholas Wagner,
Marc Coram,
Marc Berndl,
Patrick Riley,
John M. Gregoire
Publication year - 2021
Publication title -
proceedings of the national academy of sciences of the united states of america
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.011
H-Index - 771
eISSN - 1091-6490
pISSN - 0027-8424
DOI - 10.1073/pnas.2106042118
Subject(s) - workflow , oxide , transparency (behavior) , materials science , computer science , catalysis , phase (matter) , metal , nanotechnology , chemistry , database , computer security , organic chemistry , metallurgy , biochemistry
Significance Automation is accelerating the discovery of useful materials, yet testing even a small fraction of the billions of possible materials for a desired property is beyond the reach of workflows involving resource-intensive property measurements. Due to relationships among composition, structure, and properties, identifying a complex material with one interesting property makes it the proverbial needle in a haystack that merits testing for additional properties. We accelerate materials synthesis and optical characterization by employing physics-aware data science to identify materials for further investigation. With this approach, one does not need high-throughput methods for measuring every material property of interest since a single ultra-high–throughput workflow can guide material selection for other properties, which is a new paradigm for accelerated materials discovery.