Dataset Information

Discovery of complex oxides via automated experiments and data science.

ABSTRACT: The quest to identify materials with tailored properties is increasingly expanding into high-order composition spaces, with a corresponding combinatorial explosion in the number of candidate materials. A key challenge is to discover regions in composition space where materials have novel properties. Traditional predictive models for material properties are not accurate enough to guide the search. Herein, we use high-throughput measurements of optical properties to identify novel regions in three-cation metal oxide composition spaces by identifying compositions whose optical trends cannot be explained by simple phase mixtures. We screen 376,752 distinct compositions from 108 three-cation oxide systems based on the cation elements Mg, Fe, Co, Ni, Cu, Y, In, Sn, Ce, and Ta. Data models for candidate phase diagrams and three-cation compositions with emergent optical properties guide the discovery of materials with complex phase-dependent properties, as demonstrated by the discovery of a Co-Ta-Sn substitutional alloy oxide with tunable transparency, catalytic activity, and stability in strong acid electrolytes. These results required close coupling of data validation to experiment design to generate a reliable end-to-end high-throughput workflow for accelerating scientific discovery.

SUBMITTER: Yang L

PROVIDER: S-EPMC8449358 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Discovery of complex oxides via automated experiments and data science.

Yang Lusann L Haber Joel A JA Armstrong Zan Z Yang Samuel J SJ Kan Kevin K Zhou Lan L Richter Matthias H MH Roat Christopher C Wagner Nicholas N Coram Marc M Berndl Marc M Riley Patrick P Gregoire John M JM

Proceedings of the National Academy of Sciences of the United States of America 20210901 37

The quest to identify materials with tailored properties is increasingly expanding into high-order composition spaces, with a corresponding combinatorial explosion in the number of candidate materials. A key challenge is to discover regions in composition space where materials have novel properties. Traditional predictive models for material properties are not accurate enough to guide the search. Herein, we use high-throughput measurements of optical properties to identify novel regions in three ...[more]

PMID: 34508002

Similar Datasets

Project description:IntroductionWe define a designated data analytics workflow for the evaluation of stability experiments, which takes all data situations into account. This complements the evaluation described by the CLSI EP25 [1] guideline by including a targeted exception handling algorithm and thus allows one to automatically evaluate stability data based on linear regression analysis.DescriptionThe evaluation of stability experiments based on regression analysis requires the calculation of the confidence interval of the regression line. The stability time is estimated at the intersection of the confidence interval with the acceptance criterion. This approach results in solving a quadratic equation, with factors that depend on the estimated intercept, slope, the measurement variability and the chosen timepoints. When defining an automated data analytics workflow for this problem, the different cases for the solutions of the quadratic equation must be considered. For some data situations there might be no solution at all, other data situations result in a negative and a positive solution and finally there might be even two positive solutions. All these cases have to be considered for the choice of the right solution to become the estimated stability time. The CLSI EP25 [1] guideline on stability evaluation of in vitro diagnostic reagents addresses this problem only superficially and might even lead to incorrect results for some specific data scenarios.ResultsWe evaluate all possible data scenarios and provide examples for each. Based on the gained theoretical insights, we define a designated data analytics workflow and visualize it with a flowchart. By following this flowchart one can implement an automated analysis workflow, targeting all data scenarios with the appropriate exception handling.DiscussionWe deduce that the description for obtaining stability times according to CLSI EP25 is not fully adequate, as it addresses only best-case scenarios. However, for automated data analytics workflows all possible data situations have to be considered. With the here presented workflow one can program automated data analytics pipelines, which ensure that the right stability time is obtained, in case it exists. In addition all exceptions, where no stability times are present, are addressed in the right way and it provides hints as to the failure reason.

Dataset Information

Discovery of complex oxides via automated experiments and data science.

Publications

Discovery of complex oxides via automated experiments and data science.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets