Github repo for the atom-motif dual network (AMDNet)

As inspired by Pauling’s rule, we created a motif-centric learning framework by combining motif information with the atom-based graph neural networks. The atom-motif dual network (AMDNet) outperforms the state-of-the-art models in predicting the electronic structures of metal oxides. This work illustrates the route toward the fundamental design of machine learning architecture for complex materials by incorporating beyond-atom physical principles. 

The github repo link for AMDNet is here.

For the citation to the code, please refer to the following articles:


H. R. Banjade, S. Hauri, S. Zhang, F. Ricci, W. Gong, G. Hautier, S. Vucetic, Q. Yan, “Structure motif centric learning framework for inorganic crystalline systems”, Sci. Adv., 7, eabf1754 (2021)

2D materials database with symmetry-incorporated electronic structure information

We are building a 2D materials database based on ~2000 nonmetallic 2D materials extracted from the C2DB database, calculated using the HSE06 functional. What is unique about the datahbase is that all the symmetry information (irreducible representations) of electronic states have been extracted and stored in the database. This set of material information will be essential for quantum material discovery and design.

The link to the database is coming soon!