Raw data for manuscript entitled "Time-dependence of SrVO₃ thermionic electron emission properties."

In this work, we studied the poly-crystalline SrVO3 cathode activation process and its thermionic electron emission performance, cathode microstructure and surface chemistry evolution during this activation process, the operational stability over a period of 15 days, stability of the activated low effective low work function during cyclic heating and cooling process, and the short-term operational stability using a continuous emission test at operating conditions. Quantifying all of these practical stability assessments and understanding the evolution and interplay of the surface chemistry with measured effective work function are important for realizing the practical applications of SrVO3 cathode.This files contain all the figure data used for the analysis in this manuscript.</p

SI2-SSI Collaborative Research: A Computational Materials Data and Design Environment

This poster describes results associated with a project for the National Science Foundation, grant # 1148011. It was prepared for a PIs meeting on 2018-04-30. The primary results are <p>•The Materials Simulation Toolkit (MAST) for high-throughput defect and diffusion modeling</p> <p>•A Machine Learning extension (MAST-ML) to rapidly generate machine learning models from materials data.</p> <p>•Online defect and diffusion analysis apps on MaterialsHub.</p> <p>•The world’s largest computed and machine learning enhanced diffusion database with easy online search.</p> <p>•Valuable research results using these tools and data, e.g. new fuel cell materials.</p> <p>•Workforce training through the Informatics Skunkworks, an undergraduate materials informatics group.</p

SI2-SSI Collaborative Research: A Computational Materials Data and Design Environment

This poster describes results associated with a project for the National Science Foundation, grant # 1148011. It was prepared for a PIs meeting on 2018-04-30. The primary results are <p>•The Materials Simulation Toolkit (MAST) for high-throughput defect and diffusion modeling</p> <p>•A Machine Learning extension (MAST-ML) to rapidly generate machine learning models from materials data.</p> <p>•Online defect and diffusion analysis apps on MaterialsHub.</p> <p>•The world’s largest computed and machine learning enhanced diffusion database with easy online search.</p> <p>•Valuable research results using these tools and data, e.g. new fuel cell materials.</p> <p>•Workforce training through the Informatics Skunkworks, an undergraduate materials informatics group.</p