A fundamental understanding of the behavior of molten salts is important for their applications in molten salts reactors, which are considered a leading candidate for the next generation of nuclear reactors. In order to obtain accurate physical properties in molecular dynamics (MD) simulations, accurate and computationally efficient force fields are required. In recent years, machine learning (ML) techniques have been widely applied in force field development. ML force fields are able to capture many-body interactions with accuracy close to ab initio calculations, and they are not much more computationally expensive than classical force fields. ML force fields are commonly trained on ab initio MD (AIMD) simulations, but this training procedure is necessarily performed with very small simulation boxes, due to the large computational cost of AIMD simulations. The ML force field is then generally used to simulate much larger systems and calculate properties that would be difficult-to-impossible to evaluate with AIMD. Therefore, an important question to consider is whether a model trained on small simulation boxes can predict properties that can only be observed in larger systems. To rigorously answer this question, it is necessary to know the properties of the model used to generate the training data, which is not possible with AIMD. In order to investigate the effect of small training system sizes on ML force fields, we trained an ML force field for MgCl2 to reproduce an induced dipole model, named the polarizable ion model (PIM). MgCl2 was selected because the liquid structure has long range ordering that PIM simulations capture and which is not observed in small simulation boxes due to system size effects. We find that when performing the simulations in larger boxes, the ML force field reproduces the long range ordering of MgCl2 liquid, even though the force field is entirely trained from PIM simulations in small simulation boxes where such structures cannot form. Our work provides strong evidence that ML force fields of molten salts can correctly capture properties that appear on length scales beyond those observed in the training simulations.

Developing machine learning force fields of molten salts

ACS Fall 2021

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