Seminar Speaker: Dongwon Shin, ORNL

Modern Data Analytics and Supercomputing for Accelerating the Design of Advanced Structural Materials

Abstract

Emerging data analytics techniques have brought high anticipation to significantly accelerate the research and development of many scientific disciplines, including materials science. Contrary to such optimistic expectations, most domain scientists, particularly those who do not have a strong background in data science, have questions regarding deploying new tools: what can be practically achieved within the context of artificial intelligence/machine learning, and what are prerequisites? This presentation will attempt to answer these two critical questions: 1) preparing datasets constituted with clear input and output, 2) analyzing the correlation between input features and target properties to generate/validate research hypotheses, and 3) performing data analytics. An easy-to-use open-source frontend developed by Oak Ridge National Laboratory will be used to analyze the correlation, train machine learning models, and make predictions with trained surrogate models. The examples to be introduced include mechanical and oxidation properties of high-temperature alloys, thermochemical and thermophysical properties of complex oxides, and vapor processing conditions computed from high-throughput computer fluid dynamics simulations performed on a world-class supercomputer.

Biography

Dongwon Shin is a senior R&D Staff Member at Oak Ridge National Laboratory, whose research interests are computational thermodynamics, known as CALPHAD, density functional theory, and data analytics to accelerate the design and understanding of advanced materials for various energy applications. Before joining ORNL, he was a post-doctoral fellow at Northwestern University after receiving his Ph.D. at the Pennsylvania State University. He was hired as a distinguished staff fellow at ORNL via the Alvin M. Weinberg fellowship. He also won several prestigious awards, including the 2011 TMS Young Leader Award, the 2017 R&D 100 Award, and the 2018 Research Accomplishment Award by ORNL/UT-Battelle.