HPC and AI-Enabled Materials Characterization and Experimental Automation (April 2025)

Title: High Performance Computing and Artificial Intelligence Enabled Materials Characterization and Experimental Automation

Speaker: Mathew Cherukara (Computational science and AI at the Advanced Photon  Source – Argonne National Laboratory)

Date: Tuesday April 8th, 2025 – 1pm

Location: Advanced Light Source – Online. Zoom link

Host: Antoine Islegen-Wojdyla

Description: The capabilities provided by next generation light sources along with the development of new characterization techniques and detector advances are revolutionizing materials characterization (metrology) by providing the ability to perform scale-bridging, multi-modal materials characterization under in-situ and operando conditions. For example, providing the ability to image in 3D large fields of view (~mm3) at high resolution (<10 nm), while simultaneously acquiring information about structure, strain, elemental composition, oxidation state, photovoltaic response etc. 

However, these novel capabilities dramatically increase the complexity and volume of data generated. Conventional data processing and analysis methods become infeasible in the face of such large and varied data streams. The use of AI/ML methods is becoming indispensable for real-time analysis, data abstraction and decision making at advanced, high-data rate instruments. I will describe how high-performance computing (HPC) along with AI on edge devices enables real-time data analysis and self-driving experiments, creating the next generation of AI-powered materials characterization tools. 

As instrument and analysis workflows increase in complexity, large language models (LLM) have the potential to assist and enhance the productivity of scientists. I will describe early experiments with AI-powered scientific co-pilots that can provide assistance through every stage of an experiment; planning, execution, analysis and even instrument operation. 

Recording