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