Compositional transferability of deep learning potentials: a case study for LiCl-KCl melt.

Context: One of the crucial issues related to machine learning potentials is the formation of representative dataset. For multicomponent systems, it is a general methodology to scan the composition range with a certain step. However, there is a lack of information on the compositional transferability of machine learning potentials.