Conversely, a smaller batch size allows the training data of the ML model to be updated more frequently and thus enables better quality predictions. As shown in detail in the SI (Figure ), the batch size did not significantly vary model performance (most learning trajectories of different batch sizes are overlapping) and thus we propose they should be chosen in accordance with experimental workflows.