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Meeting Materials Science & Technology 2020
Symposium Materials Informatics and Modeling for 21st Century Ceramics Research
Sponsorship ACerS Basic Science Division
Organizer(s) Ming Tang, Rice University
Jeffrey M. Rickman, Lehigh University
Turab Lookman, Los Alamos National Laboratory
Scope According to the objectives of the Materials Genome Initiative (MGI), integration of data and modeling tools with experiment is a cornerstone of the materials innovation infrastructure to accelerate the discovery of advanced ceramics and other materials. Materials informatics, the application of data science to problems in materials science and engineering, is transforming the way materials research is performed and has emerged as an important tool for understanding fundamental phenomena and discovering new materials. This symposium invites scientists and engineers from academia, national laboratories and industry to discuss their latest work in materials informatics, computational modeling and their roles in advancing ceramics research in the 21st century. We welcome research talks on the development of a broad range of data analytics and modeling methodologies, and their application to the study of structure, processing to functionality of ceramic materials and beyond. Work with a strong connection to experiment is of particular interest.

Topics of interest include but are not limited to:
• Data-driven materials discovery and design for thermoelectrics, ferroelectrics, battery electrodes and electrolytes, hydrogen storage materials, high entropy alloys and other systems.
• Data analytics for microstructure classification and characterization.
• Application of multivariate statistics, machine learning and experimental design to the analysis of microscopic and spectroscopic data.
• Theory, multiscale modeling and data analysis of materials interfaces.
• Data curation, infrastructure and community development.

Abstracts Due 05/31/2020
Proceedings Plan Undecided

Coarse-grained Equation-free Time Evolution of Microstructures with Deep Learning
Predicting Stress Hotspots in Polycrystalline Materials from Microstructural Features Using Deep Learning
Using Materials Informatics to Quantify Complex Correlations Linking Structure, Properties and Processing

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