DEDD is a deep learning model trained on QEdark-EFT for crystals. The model allows quick and easy evaluation of rates of electron hole pair creation in Silicon and Germanium.
The code requires TensorFlow and NumPy.
When evaluating rates of DM induced electron hole pair creation, specify the material at the top of inference.py. Below the material the effective couplings can be specified. Finally, the DM mass is specified. These can either be set to scalars for evaluating a single DM model, or to 1D arrays for several simultanious evaluations. f.inference returns the rate of DM electron scatterings creating 1, 2, 3 and 4 electron hole pairs.