Complete and Efficient Covariants for 3D Point Configurations with Application to Learning Molecular Quantum Properties
<|reference_start|>Complete and Efficient Covariants for 3D Point Configurations with Application to Learning Molecular Quantum Properties: When modeling physical properties of molecules with machine learning, it is desirable to incorporate $SO(3)$-covariance. While such models based on low body order features are not complete, we formulate and prove general completeness properties for higher order methods, and show that $6k-5$ of these features are enough for up to $k$ atoms. We also find that the Clebsch--Gordan operations commonly used in these methods can be replaced by matrix multiplications without sacrificing completeness, lowering the scaling from $O(l^6)$ to $O(l^3)$ in the degree of the features. We apply this to quantum chemistry, but the proposed methods are generally applicable for problems involving 3D point configurations.<|reference_end|>
@article{maennel2024complete,
    title={Complete and Efficient Covariants for 3D Point Configurations with
  Application to Learning Molecular Quantum Properties},
    author={Hartmut Maennel, Oliver T. Unke, and Klaus-Robert M"uller},
    journal={arXiv preprint arXiv:2409.02730},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.02730},
    primaryClass={cs.LG physics.chem-ph}
}