Learning Regularized Positional Encoding for Molecular Prediction. (arXiv:2211.12773v1 [cs.LG])

Machine learning has become a promising approach for molecular modeling.
Positional quantities, such as interatomic distances and bond angles, play a
crucial role in molecule physics. The existing works rely on careful manual
design of their representation. To model the complex nonlinearity in predicting
molecular properties in an more end-to-end approach, we propose to encode the
positional quantities with a learnable embedding that is continuous and
differentiable. A regularization technique is employed to encourage embedding
smoothness along the physical dimension. …

arxiv encoding positional encoding prediction