April 22 - 26, 2024
Seattle, Washington
May 7 - 9, 2024 (Virtual)
Symposium Supporters
2024 MRS Spring Meeting
MT02.10.03

AI-assisted X-ray absorption spectral analysis: Data reproducibility, database, and machine Learning

When and Where

Apr 25, 2024
1:45pm - 2:00pm
Room 321, Level 3, Summit

Presenter(s)

Co-Author(s)

Deyu Lu1

Brookhaven National Laboratory1

Abstract

Deyu Lu1

Brookhaven National Laboratory1
X-ray absorption spectroscopy (XAS) is a premier element-specific experimental technique widely used for materials characterization in battery research. XAS encodes rich local structural and chemical information around X-ray absorbing species, however such information is convoluted in an abstract form, making it very difficult and time consuming to analyze. In this talk, we will discuss the emerging opportunities in AI-assisted XAS analysis, which leverages first-principles theory, high-throughput computing and data analytics. Specifically, we will highlight key areas in this approach, including pipelines to ensure data quality and reproducibility, spectral database development, and machine learning applications. The AI-assisted analysis pipeline can enable real-time feedback in high-throughput studies and autonomous experimentation.

Keywords

spectroscopy

Symposium Organizers

Alejandro Franco, Universite de Picardie Jules Verne
Deyu Lu, Brookhaven National Laboratory
Dee Strand, Wildcat Discovery Technologies
Feng Wang, Argonne National Laboratory

Symposium Support

Silver
PRX Energy

Session Chairs

Deyu Lu
Feng Wang

In this Session