Self-supervised GNN for clustering of two-dimensional materials

Ivan Savochkin, Yihao Wei, Anupam Bhattacharya, Artem Mishchenko

Keywords: self-supervised learning, GNN, 2d materials, clustering analysis, contrastive learning, structural fingerprinting, high-throughput screening, material science

TL;DR: This paper introduces a self-supervised graph neural network framework that efficiently clusters two-dimensional materials by extracting robust structural and chemical features.

Confirmation Of Submission Requirements: I submit an abstract. It uses the template provided on the submission page and is no longer than 2 pages.

Submission Number: 161