AL4GAP: Active Learning Workflow for generating DFT-SCAN Accurate Machine-Learning Potentials for Combinatorial Molten Salt Mixtures

Machine learning interatomic potentials have emerged as a powerful tool for bypassing the spatio-temporal limitations of ab initio simulations, but major challenges remain in their efficient parameterization. We present AL4GAP, an active learning software workflow for generating multi-composition Gaussian approximation potentials (GAP) for arbitrary molten salt mixtures. The workflow capabilities includes: (1) setting up user-defined combinatorial chemical spaces of charge neutral mixtures of arbitrary molten mixtures spanning 11 cations (Li, Na, K, Rb, Cs, Mg, Ca, Sr, Ba and two heavy species, Nd and Th) and 4 anions (F, Cl, Br and I), (2) configurational sampling using low-cost empirical parameterizations, (3) ensemble active learning for down-selecting configurational samples for single point density functional theory calculations at the level of strongly constrained and appropriately normed (SCAN) exchange-correlation functional, and (4) Bayesian optimization for hyperparameter tuning of two-body and many-body GAP models. We apply the workflow to showcase high throughput generation of five independent GAP models for multi-composition binary-mixture melts, each of increasing complexity with respect to charge valency and electronic structure, namely: LiCl-KCl, NaCl-CaCl2, KCl-NdCl3, CaCl2-NdCl3 and KCl-ThCl4. Our results indicate that GAP models, can accurately predict structure for diverse molten salt mixture with DFT-SCAN accuracy, capturing the intermediate range ordering characteristic of the multi-valent cationic melts.