We note that there might be a way to resolve this issue, namely by improving feature engineering. For example, a recent work tried to include the attention mechanism in the GCNN for crystal properties predictions . Nevertheless, in that study, the resulting elasticity prediction improvement was not significant. Another approach might be to utilize the surrogate model to accurately predict atomic-scale potential energy surfaces, and to calculate elasticity by applying the strain tensors. Such an approach, similar in spirit to the neural network potentials by Behler and Parrinello , requires accurate predictions of stress from energy and volume under the deformation by the strain tensors. The generation of reference data in this way will likely require more effort and at least partial human supervision, in order to cover all of the configurational space.