In the final refinement stage, two ADAM optimizers were initialized separately, each using either density or enthalpy gradients to update. The parameter updates from both optimizers are used alternately during the optimization. In our experience, this strategy ensures that different physical data with different magnitudes do not interfere with each other in gradient updates. Moreover, arbitrary relative weights can be avoided between different experimental data types.