where E disp is the empirical D2 dispersion correction and E coul is a truncated Coulomb energy, which decays smoothly at shortrange . The forces can be expressed as the first derivative of E T with respect to the atomic positions. To train a QRNN model, we used a training:validation split of 99:1 and a learning rate of 2 × 10 -3 , and the optimized QRNN model was obtained by minimizing the loss functions of the predicted energies and forces with respect to the DFT training data.