Colloquium

Materials informatics of structural metals and alloys

Engineering performance of materials for advanced load-bearing applications are strongly dependent on their internal structure (microstructure).  Quantitative microstructure--property relationships are key for efficient design of new high-performance materials. Machine learning is a promising approach for accelerated data-driven development of materials. Complex and hierarchical nature of the microstructure as well as limited data availability often hinder the use of off-the-shelf machine learning methods. In this talk, I will discuss new approaches to quantification of microstructures for enabling machine learning of microstructure-property relationships, including graph representation of grain structures of alloys. 

Place: Hybrid: Math, 501 and Zoom https://arizona.zoom.us/j/86997964863     Password:   Locute

When

2 p.m. Jan. 28, 2022

Where

Hybrid: Math, 501/Online