Fig. 13

Overview of the concept of “active learning” [153]. NNP models with differentiating parameters are trained on an initial reference dataset, attempting to capture all the atomic environments relevant for the system of interest to be simulated. Through validation of these models, it is possible to determine what data needs to be added to the reference datasets for further refinement of the NNP models. The models try to learn to describe an unknown potential energy landscape (red and green curves vs. black curve of top right graph). In the conformational regions where the predicted curves differentiate, more information should be provided to the models. These conformations can be obtained through additional MD simulations of the reference structures to provide additional atomic environments for further training. When all trained models converge to describe one potential energy curve (overlapping curves of bottom right graph), the NNPs are optimized, and a final architecture can be selected for the actual application