PU-CGCNN

Predicts a Crystal-Likeness Score (CLscore) for inorganic crystal structures using positive–unlabeled learning with bagged crystal graph convolutional neural networks (PU-CGCNN).

CLscore ∈ [0, 1] estimates how “crystal-like” / synthesizable a structure is relative to experimentally reported materials. It is complementary to thermodynamic filters such as energy above hull — high CLscore does not guarantee experimental success, but is a useful soft prior for screening and generative filtering.

Model: Jang et al., JACS 2020 (pretrained 100-bag ensemble). Paper: https://doi.org/10.1021/jacs.0c07384 Code: https://github.com/kaist-amsg/Synthesizability-PU-CGCNN