Work

Enhancing transfer learning in angle-resolved photoemission spectroscopy (ARPES) with spatially-aware representations via graph convolution

Year: 2026

Type: article

Abstract: A recent application of machine learning has been to spatially-resolved angle-resolved photoemission spectroscopy (ARPES). Here we advance the state-of-the-art by applying representational learning to transform ARPES data into an embedding space of a pre-trained self-supervised learning model, thus enhancing the pipeline that improves the bandstructure classification and domain assignment/segmentation performance compared to a k -means clustering method. In the current iteration, the real-space information is entered into the domain assignment through the graph convolution method, which improves the transfer learning performance of the original self-supervised model. Lastly, an unsupervised automated tool is developed that incorporates these techniques to enable automatic domain assignment.

Source: Machine Learning Science and Technology

DOI: 10.1088/2632-2153/ae3fe7

Authors: Hendrik Santoso Sugiarto, Sandy Adhitia Ekahana, Bryan Christofer Wijaya, Genta Indra Winata, Y Soh, G Aeppli