The in vacuo ADP-specific model (Δ-ML) was obtained using a delta-learning approach with the ff19SB MM energy as the low level of theory and ωB97X/6-31G* DFT as the reference. Sparse Gaussian Approximation Potential with quadratic SOAP kernel was used as the model architecture, and training was performed using the librascal package (). The initial training set was obtained as follows. A single frame was taken from each window of the MM US calculation. For each frame, the ADP geometry was extracted and a short in vacuo DFT trajectory of 50 fs at 500 K was propagated, keeping the same RC restraints as in the corresponding US window. The elevated temperature is used to enhance sampling of the conformational space. As a result, the bonds and angles are relaxed to the DFT potential. For the relaxed in vacuo ADP structures, single point DFT calculations were performed, storing the total energy. The resulting 1296 structures with the corresponding DFT energies were used as the initial input to the iterative active learning procedure. At each iteration, the basis set of atomic environments for Sparse Gaussian Process regression (GPR) is selected with the Informative Vector Machine (IVM) procedure. Then, the GAP potential is built by training on 80% of the structures in the training set with the prediction error being calculated over the remaining 20%. If the resulting error was below 1.5 kcal•mol -1 , the potential was considered to be converged and the procedure stops. Otherwise, the trained potential is used to propagate short MD trajectories at 500 K (20-100 fs depending on the iteration). Single point DFT calculations are then performed for the resulting structures and are added to the training set and the procedure is repeated.