Yes I’ve tried that
I did it in my 1st approach to solving the problem with no feature extraction
It was doing poorly, this was understandable because the data points are really small, sth like: 0.33 x 10^-20 or so
So all I just did was multiply all input by 10^19, and I started to get training accuracy of 95 to 100% but testing accuracy of 50 to 56%.
And this scaling  also affects the testing data
However, the normalization I tested in this approach today was torch.exp(n)/100 and it made the data value distribution more balanced than before