Name: Brookhaven AIMS Series: Controlled projection using generative AI and its application to XAS
Start: 2024-04-09T12:00:00-04:00
End: 2024-04-09T13:00:00-04:00
Location: No location set

Brookhaven AIMS Series: Controlled projection using generative AI and its application to XAS

Tuesday, 9 April 2024 - 12:00

Monday, 8 April 2024
Tuesday, 9 April 2024
12:00 Controlled projection using generative AI and its application to XAS - Xiaohui Qu (Brookhaven National Laboratory)
Controlled projection using generative AI and its application to XAS
- Xiaohui Qu (Brookhaven National Laboratory)
12:00 - 13:00
**Abstract**: In this talk, I will introduce a new machine-learning method to discover structure-property relationships in complex materials data. The distillation of structure-property is a process projecting the changes of material properties to a controlled variable represented by a structured descriptor. Adversarial Autoencoder is a generative model that can be used to create a virtual chemical space that is easier to explore. We achieved controlled projection by leveraging prior physical knowledge to constrain the latent space so that each dimension tracks a targeted structure descriptor. The utility of this approach is illustrated for X-ray absorption spectra (XAS) of 3d transition metal oxides in exploring how the shape of an XAS spectrum changes in response to the change of a structure descriptor. The new method recovers trends established in the literature and reveals new ones. **Bio**: Dr. Xiaohui Qu is a Staff Scientist at CFN. He works in the Theory and Computation Group focusing on research, development, and implementation of data analytics techniques. Dr. Qu is a computational chemist by training, with strong accomplishments in specific areas of chemistry, energy storage, machine learning, and X-ray absorption spectroscopy. He combines materials science research with extensive experience in deep learning techniques and a data-centric approach.