Integrating Chemistry and Experimental Knowledge into Deep Learning Models for Classification of Structural Defects — ASN Events

Integrating Chemistry and Experimental Knowledge into Deep Learning Models for Classification of Structural Defects — ASN Events

Integrating Chemistry and Experimental Knowledge into Deep Learning Models for Classification of Structural Defects

Integrating Chemistry and Experimental Knowledge into Deep Learning Models for Classification of Structural Defects (114705)

Jiadong Dan ¹ , Cheng Zhang ² , Duane Loh ¹

National University of Singapore, Singapore, SINGAPORE
City University of Hong Kong (CityU), Hong Kong, China

Authors contributing to this presentation.

Dan, J

Jiadong Dan an Eric and Wendy Schmidt AI in Science Fellow at the National University of Singapore (NUS), specializing in the intersection of machine learning (ML) and scanning transmission electron microscopy (STEM). Guided by Prof. Stephen J. Pennycook, my Ph.D. research innovatively applied ML to identify quantum defects in atomic resolution STEM images. Now in Assoc. Prof. Duane Loh’s group, I am advancing a robust ML framework to characterize materials through a hierarchy of structural motifs. Leveraging my deep expertise, I aim to develop an electron microscope copilot system to improve material analysis and enhance understanding of material disorders.

Zhang, C

Loh, D