Automated high-throughput screening of 2D materials using GNNs

This workflow combines a pretrained graph model with uncertainty-aware acquisition to rank 2D candidates for hydrogen evolution and thermal stability. They evaluate around 70,000 hypothetical structures and only run expensive DFT for the top uncertainty-calibrated subset. The hit rate seems significantly better than random or heuristic filtering.

One concern is that the candidate generator may bias towards known motifs, limiting novelty. Still, the closed-loop process is a nice template for groups that cannot afford brute-force DFT on full candidate spaces.