While there have recently been significant advancements in neural network potentials (NNPs) trained on AIMD data, 62,63 their computational cost remains relatively high for long simulation runs of crystal growth. Additionally, the complexity of modeling interfaces may lead to inaccuracies when using NNPs, as they often require extrapolation from training data.