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 spatiotemporal limitations of ab initio simulations, but major challenges remain in their efficient parameterization. We present AL4GAP, an ensemble active learning software workflow for generating multicomposition Gaussian approximation potentials (GAP) for arbitrary molten salt mixtures. The workflow capabilities include: (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) 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 AL4GAP workflow to showcase high throughput generation of five independent GAP models for multicomposition 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 density functional theory (DFT)-SCAN accuracy, capturing the intermediate range ordering characteristic of the multivalent cationic melts.

Automated Development of Molten Salt Machine Learning Potentials: Application to LiCl.

The in silico modeling of molten salts is critical for emerging "carbon-free" energy applications but is inhibited by the cost of quantum mechanically treating the high polarizabilities of molten salts. Here, we integrate configurational sampling using classical force fields with active learning to automate and accelerate the generation of Gaussian approximation potentials (GAP) for molten salts. This methodology reduces the number of expensive ab initio evaluations required for training set generation to O(100), enabling the facile parametrization of a molten LiCl GAP model that exhibits a 19 000-fold speedup relative to AIMD. The developed molten LiCl GAP model is applied to sample extended spatiotemporal scales, permitting new physical insights into molten LiCl's coordination structure as well as experimentally validated predictions of structures, densities, self-diffusion constants, and ionic conductivities. The developed methodology significantly lowers the barrier to the in silico understanding and design of molten salts across the periodic table.

A Phase-Change Mechanism of GST-SL Based Superlattices upon Sb Flipping.

Reversible phase-change behaviors of Ge-Sb-Te based superlattices (GST-SL) were studied by ab initio molecular dynamics (AIMD) simulations based on three models containing Ge/Sb intermixing, namely the Petrov-mix, Ferro-mix, and Kooi-mix models. The flipping behavior of Sb atoms was found in all the three GST-SL models in the melting process. Among them the Kooi-mix model exhibited the best stability, and the analyses of bond length distribution and electron localization function provided a better explanation on the phase transition of GST-SL. Finally, we proposed a fast switching model for GST-SL based on Sb flipping.

Fluid Structure of Molten LiCl-Li Solutions.

Molten mixtures of lithium chloride and metallic lithium (LiCl-Li) play an essential role in the electrolytic reduction of various metal oxides. These mixtures possess unique high temperature physical and chemical properties that have been investigated for decades. However, due to their extreme chemical reactivity, no study to date has been capable of definitively proving the basic physical nature of Li dissolution in molten LiCl. In this study, the evolution of the structure of molten LiCl-Li is probed as metallic Li is electrochemically introduced into the melt in situ, using synchrotron radiation experiments based on high energy wide-angle X-ray scattering (WAXS) and small-angle X-ray scattering (SAXS). The time-resolved scattering results indicate the formation of transient Cl- ion cages surrounding low-density voids with a periodicity of ∼8.3 Å, which suggests the formation of metastable Li nanocluster. The structure of the LiCl-Li nanoclusters in the solution is modeled using ab initio molecular dynamics (AIMD) simulations. The simulation results are in agreement with the X-ray diffraction measurement and suggest the nanoclusters are predominantly Li8, along with smaller clusters.