Integrated Microscopy – Simulation Data Analytics for AI-Guided Advanced Materials and Devices
Project Description
In the proposed project, the involved researcher will directly integrate time-dependent SEM, TEM, x-ray, and crystallographic orientation spatial data as a means to: a) develop physically, consistent microstructural evolution models; b) parameterize properties and relevant topological features; c) propose advanced processing conditions, microstructure designs, and advanced microstructural mechanisms as they involve multiphysical mechanical, surface tension, and thermochemical interactions. The project will integrate a wide variety of data analytics and machine, learning strategies, as a steppingstone to automatically simulate and then design the analyzed material. That developed, automated understanding will be used to develop a feedback loop between processing and simulation that allows the automated optimization of the experiment.
Start Date
Fall 2026
Postdoc Qualifications
The qualified candidate would have a strong background on phase field and microstructural modeling, and the ability (and interest) to rapidly integrate experimental microscopy data into microstructural models. A background on Machine Learning models will also be important.
Co-advisors
Edwin García, redwing@purdue.edu; School of Materials Engineering (MSE)
Haiyan Wang, hwang00@purdue.edu; School of Electrical and Computer Engineering (ECE)
Bibliogaphy
Huang J, Zhang B, Hermawan D, Sanjuan A, Tsai BK, Huang J, Garcı́a RE, Wang H. Complex Oxide‐metal Hybrid Metamaterials with Integrated Magnetic and Plasmonic Non‐noble Metal Nanostructures. Advanced Functional Materials. 2025 Mar 10:2500741.
Lund J, Wang H, Braatz RD, García RE. Machine learning of phase diagrams. Materials Advances. 2022;3(23):8485-97.