  Machine learning over-fitting caused by data scarcity greatly limits the
application of machine learning for molecules. Due to manufacturing processes
difference, big data is not always rendered available through computational
chemistry methods for some tasks, causing data scarcity problem for machine
learning algorithms. Here we propose to extract the natural features of
molecular structures and rationally distort them to augment the data
availability. This method allows a machine learning project to leverage the
powerful fit of physics-informed augmentation for providing significant boost
to predictive accuracy. Successfully verified by the prediction of rejection
rate and flux of thin film polyamide nanofiltration membranes, with the
relative error dropping from 16.34% to 6.71% and the coefficient of
determination rising from 0.16 to 0.75, the proposed deep spatial learning with
molecular vibration is widely instructive for molecular science. Experimental
comparison unequivocally demonstrates its superiority over common learning
algorithms.
