To preserve spin symmetry in the NN, which receives spin-resolved features, the features are passed through the network twice during both training and prediction. In the first pass, the features are presented in the normal order (α, β), and in the second pass, the order is reversed (β, α). The final prediction of the NN is then obtained by averaging the results from both passes. This approach ensures that the model treats spin symmetrically and avoids bias toward a specific spin configuration. This method has been proposed originally in ref. 40 into superior performance when evaluated on the much larger GMTKN55 benchmark.