Following the selection of the best network, a series of simulations are run to actively sample the configurational space described by the selected NN as shown in Fig. . For the purposes of this work in the first stage, a Boltzmann based Metropolis Monte Carlo sampling and a modified nested ensemble based approach 63 were used to generate the structures for each AL iteration. It was found in past research ( 64 ) that simple thermal sampling is not sufficient in properly exploring the configurational space. The success of an active learning scheme is based on the algorithm's ability to probe for configurations that are incorrectly predicted. Thermal sampling tends to sample only over a narrow range of thermally relevant structures. However, naive sampling approaches such as purely random cluster generation with no care given to energetics is often no better as this encounters the opposite problem in that it over biases the sampling toward entropically favored configurations while ignoring lower energy and other physical relevant states. As such the Modified Nested Ensemble which works by continuously halving the density of states was found to be a nice compromise between pure thermal or purely random sampling. The Metropolis simulation was run for 5, 000 MC cycles with 30 moves per cycle at 300K with the initial structure being randomly picked from the current neural network training pool. The Nested Ensemble simulations were run for another 24,000 cycles with 30 moves per cycle. Such a larger sampling through the nested sampling was required such that for elements with configurations with more diverse energy ranges would be covered completely and the structures representing the large range may be tested against the NN in the next step F. Testing the NN After the stochastic sampling step is completed, a set of 10 structures are gathered from the trajectory files of the Metropolis and a set of 15 structures are gathered from Nested Sampling trajectory files. These are sampled by dumping a structure every 1000 cycles for the metropolis run and for the nested sampling run dumping a structure every 1600 cycles. For the nested sampling run this is set up such that we pull one structure from each energy "strata" as the nested sampling gradually constricts the energy space. This ensures we are always testing structures ranging from very high energy down towards more equilibrium and low energy regions of the configurational phase space. The primary goal of this is to expose structures that the model incorrectly labels. In stage 1 the real energies of the structures were calculated using Sutton Chen EAM potentials and compared with the predictions of the NN model. Each time the energies do not agree within a specific tolerance that structure is added to the training set of structures. here we use a variable tolerance scheme where the tolerance of each cycle is determined as the 105% training error for that cycle the value for which can be obtained from the AENet log files for the training. This ensures that the structures whose energies are predicted catastrophically wrong compared to the training error are added into the training set each cycle. This ensures that in the initial stages of active learning all relevant structures in both the highly stable regime as well as the higher energy configurations get added to the training set at a higher pace as well as, in the later stages of the active learning slows down the addition of structures to the training set, reducing the risk of the network overfitting. This entire process is continued until the exit criteria is hit. For this work, we specified that if no new structures were added in 9 consecutive AL iterations, that the potential has converged. The converged NN potential following the stage 1 was validated extensively with a validation set of 35000 structures which spanned the entire range of energies the network was trained in.