Publication: (Filling et al 2025) - Direct Molecular Polarizability Prediction with SO(3)-Equivariant Local Frame GNNs

The paper describes a graph neural network architecture that uses local frames to predict local geometric properties..

Data and Resources

Field	Value
Publication(s)	Publication 1 Title of related publication Direct Molecular Polarizability Prediction with SO(3)-Equivariant Local Frame GNNs Year of related publication 2025 DOI of related publication (not DOI of data resources)
Funding	DFG Project No. 233630050-TRR146
Subproject	Project B7: Automated model building and representation learning for multiscale simulations
Cooperation partner(s)	Johannes Gutenberg University Mainz Max Planck Institute for Polymer Research
Responsible Person's Name (PI)	Prof. Dr. Michael Wand
Responsible Person's Email for further data requests	wandm@uni-mainz.de
Responsible Person's Affiliation	Institute of Computer Science, Johannes Gutenberg University Mainz, Germany