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Recent advances in machine learning (ML) are expediting materials discovery and design. One significant challenge facing ML for materials is the expansive combinatorial space of potential materials formed by diverse constituents and their flexible configurations. This complexity is particularly evident in molecular mixtures, a frequently explored space for materials, such as battery electrolytes. Owing to the complex structures of molecules and the sequence-independent nature of mixtures, conventional ML methods have difficulties in modeling such systems. Here, we present MolSets, a specialized ML model for molecular mixtures, to overcome the difficulties. Representing individual molecules as graphs and their mixture as a set, MolSets leverages a graph neural network and the deep sets architecture to extract information at the molecular level and aggregate it at the mixture level, thus addressing local complexity while retaining global flexibility. We demonstrate the efficacy of MolSets in predicting the conductivity of lithium battery electrolytes and highlight its benefits in the virtual screening of the combinatorial chemical space. Published by the American Physical Society2024 

Abstract Despite the potential to become the next‐generation energy storage technology, practical lithium–sulfur (Li–S) batteries are still plagued by the poor cyclability of the lithium‐metal anode and sluggish conversion kinetics of S species. In this study, lithium tritelluride (LiTe₃), synthesized with a simple one‐step process, is introduced as a novel electrolyte additive for Li–S batteries. LiTe₃quickly reacts with lithium polysulfides and functions as a redox mediator to greatly improve the cathode kinetics and the utilization of active materials in the cathode. Moreover, the formation of a Li₂TeS₃/Li₂Te‐enriched interphase layer on the anode surface enhances ionic transport and stabilizes Li deposition. By regulating the chemistry on both the anode and cathode sides, this additive enables a stable operation of anode‐free Li–S batteries with only 0.1 mconcentration in conventional ether‐based electrolytes. The cell with the LiTe₃additive retains 71% of the initial capacity after 100 cycles, while the control cell retains only 23%. More importantly, with high utilization of Te, the additive enables significantly better cyclability of anode‐free pouch full‐cells under lean electrolyte conditions. 

Abstract Non‐volatile phase‐change memory (PCM) devices are based on phase‐change materials such as Ge₂Sb₂Te₅ (GST). PCM requires critically high crystallization growth velocity (CGV) for nanosecond switching speeds, which makes its material‐level kinetics investigation inaccessible for most characterization methods and remains ambiguous. In this work, nanocalorimetry enters this “no‐man's land” with scanning rate up to 1 000 000 K s⁻¹(fastest heating rate among all reported calorimetric studies on GST) and smaller sample‐size (10–40 nm thick) typical of PCM devices. Viscosity of supercooled liquid GST (inferred from the crystallization kinetic) exhibits Arrhenius behavior up to 290 °C, indicating its low fragility nature and thus a fragile‐to‐strong crossover at ≈410 °C. Thin‐film GST crystallization is found to be a single‐step Arrhenius process dominated by growth of interfacial nuclei with activation energy of 2.36 ±  0.14 eV. Calculated CGV is consistent with that of actual PCM cells. This addresses a 10‐year‐debate originated from the unexpected non‐Arrhenius kinetics measured by commercialized chip‐based calorimetry, which reports CGV 10³−10⁵higher than those measured using PCM cells. Negligible thermal lag (<1.5 K) and no delamination is observed in this work. Melting, solidification, and specific heat of GST are also measured and agree with conventional calorimetry of bulk samples.