“High-throughput Experimentation and Holistic Integration with Computational Date for Accelerated Alloy Design”

Abstract:

Experimental techniques for rapid collections of materials data and holistic approaches to integrate experimental and computational data will be described with examples. Localized property measurements on composition gradients created in diffusion multiples allow high-throughput collection of several materials properties as a function of composition, in addition to phase diagrams and diffusion coefficients. A novel approach was developed to establish reliable diffusion coefficient (atomic mobility) databases by holistically integrating both experimental and computational data. This approach together with much simplified models for diffusion
coefficient will enable more reliable diffusion databases to be established rapidly for various simulations of materials processes. An approach that iteratively and holistically integrate experimental results with model predictions can be very effective in both establishing materials databases and accelerating alloy design.

Biography:

Dr. JC Zhao has been a Minta Martin Professor and Chair of the MSE Department at the University of Maryland since July 2019. He was a Program Director at DOE ARPA-E from 2014 to 2017 and was a professor at Ohio State from 2008 to 2014 and also from 2017-2019. Before academia, Dr. Zhao was a materials scientist
at GE Research Center for 12 years (1995-2007) where he introduced Thermo-Calc to GE 25 years ago and initiated the diffusion multiple approach for accelerated materials development. Zhao holds 49 issued U.S. patents and was the 2001 winner of the prestigious Hull Award from GE and a 2022 Humboldt Research Award. He is a Fellow of both ASM and MRS,and received the 2021 TMS William Hume-Rothery Award that “is presented annually to recognize a scientific leader for exceptional scholarly contributions to the science of alloys”.