|About this Abstract
||2019 TMS Annual Meeting & Exhibition
||Computational Approaches for Big Data, Artificial Intelligence and Uncertainty Quantification in Computational Materials Science
||Materials Informatics and Big Data: Realization of 4th Paradigm of Science in Materials Science
||Ankit Agrawal, Alok Choudhary
|On-Site Speaker (Planned)
In this age of “big data”, large-scale experimental and simulation data is increasingly becoming available in all fields of science, and materials science is no exception. The need to use advanced data science approaches in materials science is also recognized by the Materials Genome Initiative (MGI), further promoting the emerging field of materials informatics. In this talk, I would present some of our recent works employing state-of-the-art data analytics including deep learning for exploring processing-structure-property-performance (PSPP) linkages in materials, both in terms of forward models (e.g. predicting property for a given material) and inverse models (e.g. discovering materials that possess a desired property). I will also demonstrate some online web-tools we have developed that deploy machine learning models to predict materials properties. Such data-driven analytics can significantly accelerate prediction of material properties, which in turn can accelerate the optimization process and thus help realize the dream of rational materials design.
||Planned: Supplemental Proceedings volume