In a bid to increase the computational efficiency of the approach, we investigated the use of larger batch sizes during active learning (the batch size used so far is 1 atom). We explored two strategies to generate larger batches: mini-batch, which generates a batch from a defined number of most informative atoms, and diverse mini-batch, which composes a batch from a defined number of diverse, most informative atoms (identified by k-means 35 clustering). For mini-batch, we explored batch sizes of 5, 10, 25, and 100 atoms; for diverse mini-batch, we explored batches generated from the 5 most diverse atoms selected from the 25 most informative atoms, and from the 10 most diverse atoms selected from the 100 most informative atoms. Following the identical active learning setup as in the previous experiments, the use of batch sizes of up to 100 showed similar trends for the performance progressions (Figure ). The diverse mini-batch sampling did not show an advantage (e.g. a steeper performance increase) over the standard mini-batch approach. From this, we conclude that the addition of small batches of atoms instead of single atoms is well-tolerated and leads to a substantial speed-up of the active learning process.