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1. PolymerGNN

PolymerGNN is a deep learning method to predict polymer properties. This model utilizes state-of-the-art graph neural networks (GNNs), which encode molecular graphs into fixed-length vectors.

2. Molecular Representations

Hands-on examples that accompany our eBook on molecular representations for machine learning applications.

3. Persistent Images

Persistent images (PIs) are a molecular representations for chemical machine learning applications based on persistent homology, an applied branch of topology. 

Application of PIs on a database with ~181k Fe(IV)-oxo complexes + Similarity comparison based on PIs

4. Data-driven Quantum Chemistry

Hands-on examples on data-driven coupled-cluster (DDCC) and data-driven complete active space second-order perturbation theory (DDCASPT2).

5. Data-driven v2RDM

Repository of the data-driven variational 2RDM (v2RDM) project with an example code.