So3krates: Equivariant attention for interactions on arbitrary length-scales in molecular systems

Understand

The application of machine learning methods in quantum chemistry has enabled the study of numerous chemical phenomena, which are computationally intractable with traditional ab-initio methods.

• However, some quantum mechanical properties of molecules and materials depend on non-local electronic effects, which are often neglected due to the difficulty of modeling them efficiently.
• This work proposes a modified attention mechanism adapted to the underlying physics, which allows to recover the relevant non-local effects.
• Namely, we introduce spherical harmonic coordinates (SPHCs) to reflect higher-order geometric information for each atom in a molecule, enabling a non-local formulation of attention in the SPHC space.