The DNN potential was constructed based on the smooth version of the Deep Potential Molecular Dynamics (DPMD) method, as implemented in the DeepMD-Kit package. The DNN architecture was composed of a 3-layers-deep neural network with 120 neurons each. The DNN model was trained with reinforcement learning. Within this training scheme, the DNN model is iteratively optimized with cycles composed of 1) extensive exploration of configurational space using DPMD simulation; 2) selection of a few configurations with large uncertainty of force prediction; and 3) fitting DNN models using new data. For item 1, liquid ammonia, liquid CO 2 and ammonia-CO 2 mixtures were explored. Enhanced sampling was also used to collect data of the chemical reactivity of CO 2 with liquid ammonia.