On the other hand, mat2vec style embedding vectors are expected to be encoded with some underlying materials science knowledge that can help an ML model to learn a great deal even from a small number of data points. So the rate of deterioration of errors of a model constructed using our features will be expected to be less than that of a model constructed using only one-hot encoding.