Computational Design of Low Melting Eutectics of Molten Salts: A Combined Machine Learning and Thermodynamic Modeling Approach.

We develop a computational framework combining thermodynamic and machine learning models to predict the melting temperatures of molten salt eutectic mixtures (Teut). The model shows an accuracy of ∼6% (mean absolute percentage error) over the entire data set. Using this approach, we screen millions of combinatorial eutectics ranging from binary to hexanary, predict new mixtures, and propose design rules that lead to low Teut. We show that heterogeneity in molecular sizes, quantified by the molecular volume of the components, and mixture configurational entropy, quantified by the number of mixture components, are important factors that can be exploited to design low Teut mixtures. While predicting eutectic composition with existing techniques had proved challenging, we provide some preliminary models for estimating the compositions. The high-throughput screening technique presented here is essential to design novel mixtures for target applications and efficiently navigate the vast design space of the eutectic mixtures.

Discovery of novel Li SSE and anode coatings using interpretable machine learning and high-throughput multi-property screening.

All-solid-state batteries with Li metal anode can address the safety issues surrounding traditional Li-ion batteries as well as the demand for higher energy densities. However, the development of solid electrolytes and protective anode coatings possessing high ionic conductivity and good stability with Li metal has proven to be a challenge. Here, we present our informatics approach to explore the Li compound space for promising electrolytes and anode coatings using high-throughput multi-property screening and interpretable machine learning. To do this, we generate a database of battery-related materials properties by computing [Formula: see text] migration barriers and stability windows for over 15,000 Li-containing compounds from Materials Project. We screen through the database for candidates with good thermodynamic and electrochemical stabilities, and low [Formula: see text] migration barriers, identifying promising new candidates such as [Formula: see text]N, [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text], among others. We train machine learning models, using ensemble methods, to predict migration barriers and oxidation and reduction potentials of these compounds by engineering input features that ensure accuracy and interpretability. Using only a small number of features, our gradient boosting regression models achieve [Formula: see text] values of 0.95 and 0.92 on the oxidation and reduction potential prediction tasks, respectively, and 0.86 on the migration barrier prediction task. Finally, we use Shapley additive explanations and permutation feature importance analyses to interpret our machine learning predictions and identify materials properties with the largest impact on predictions in our models. We show that our approach has the potential to enable rapid discovery and design of novel solid electrolytes and anode coatings.

Solid-solid phase transformations induced through cation exchange and strain in 2D heterostructured copper sulfide nanocrystals.

We demonstrate dual interface formation in nanocrystals (NCs) through cation exchange, creating epitaxial heterostructures within spherical NCs. The thickness of the inner-disk layer can be tuned to form two-dimensional (2D), single atomic layers (<1 nm). During the cation exchange reaction from copper sulfide to zinc sulfide (ZnS), we observe a solid-solid phase transformation of the copper sulfide phase in heterostructured NCs. As the cation exchange reaction is initiated, Cu ions replaced by Zn ions at the interfaces are accommodated in intrinsic Cu vacancy sites present in the initial roxbyite (Cu1.81S) phase of copper sulfide, inducing a full phase transition to djurleite (Cu1.94S)/low chalcocite (Cu2S), a more thermodynamically stable phase than roxbyite. As the reaction proceeds and reduces the size of the copper sulfide layer, the epitaxial strain at the interfaces between copper sulfide and ZnS increases and is maximized for a copper sulfide disk ∼ 5 nm thick. To minimize this strain energy, a second phase transformation occurs back to the roxbyite phase, which shares a similar sulfur sublattice to wurtzite ZnS. The observation of a solid-solid phase transformation in our unique heterostructured NCs provides a new pathway to control desired phases and an insight into the influence of cation exchange on nanoscale phase transitions in heterostructured materials.

Nanoparticle metamorphosis: an in situ high-temperature transmission electron microscopy study of the structural evolution of heterogeneous Au:Fe2O3 nanoparticles.

High-temperature in situ electron microscopy and X-ray diffraction have revealed that Au and Fe2O3 particles fuse in a fluid fashion at temperatures far below their size-reduced melting points. With increasing temperature, the fused particles undergo a sequence of complex structural transformations from surface alloy to phase segregated and ultimately core-shell structures. The combination of in situ electron microscopy and spectroscopy provides insights into fundamental thermodynamic and kinetic aspects governing the formation of heterogeneous nanostructures. The observed structural transformations present an interesting analogy to thin film growth on the curved surface of a nanoparticle. Using single-particle observations, we constructed a phase diagram illustrating the complex relationships among composition, morphology, temperature, and particle size.

(NH4)2S, a highly reactive molecular precursor for low temperature anion exchange reactions in nanoparticles.

Ammonium sulfide ((NH4)2S) exhibits high reactivity as a sulfide reagent in anion exchange reactions that transform CoO to cobalt sulfide nanoparticles (NPs). The faster diffusion of Co(2+) and O(2-) than the incoming S(2-) during the anion exchange causes a significant expansion of the NP voids. The low temperature (70 °C) anion exchange reaction produces amorphous cobalt sulfide NPs with Co : S ratio of ca. 3 : 4, which are converted into crystalline NPs with a major phase of cubic Co3S4 by annealing at high temperature in an organic solution.