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Abstract Superionic conductors, includingACrX₂(A=Ag, Cu; X = S, Se) compounds, have attracted attention due to their low lattice thermal conductivity and high ionic conductivity. These properties are driven by structural characteristics such as anharmonicity, soft bonding, and disorder, which enhance both fast ion transport and thermal resistance. In the present study, we investigate the impact of various factors (e.g.A-site disorder, microstructure, speed of sound and chemical composition) on the thermal conductivity of the compounds CuCrS₂, CuCrSe₂, AgCrS₂and AgCrSe₂. The samples were synthesized using solid state reaction, ball milling and subsequent spark plasma sintering, and thermal diffusivity, electrical resistivity, Hall coefficients and Seebeck coefficients were measured as a function of temperature. The selenides were found to behave as degenerate semiconductors, with reasonable thermoelectric figure of merit (up to 0.79 in CuCrSe₂), while the sulfides behaved as non-degenerate semiconductors with high electrical resistivity. At room temperature, all samples are in the ordered phase and show low lattice thermal conductivity ranging from 0.60 W m⁻¹-K in AgCrSe₂to 1.1 W m⁻¹-K in CuCrSe₂. Little reduction in lattice thermal conductivity was observed in the high-temperature phase, despite the increased disorder on the cation site and the onset of superionic conductivity. This suggests that the low lattice thermal conductivity inACrX₂compounds is an inherent property of the crystal structure, caused by anharmonic bonding and diffuson dominated transport. 

Abstract Research shows that user traits can modulate the use of visualization systems and have a measurable influence on users' accuracy, speed, and attention when performing visual analysis. This highlights the importance of user‐adaptive visualization that can modify themselves to the characteristics and preferences of the user. However, there are very few such visualization systems, as creating them requires broad knowledge from various sub‐domains of the visualization community. A user‐adaptive system must consider which user traits they adapt to, their adaptation logic and the types of interventions they support. In this STAR, we survey a broad space of existing literature and consolidate them to structure the process of creating user‐adaptive visualizations into five components: Capture ⒶInputfrom the user and any relevant peripheral information. Perform computational ⒷUser Modellingwith this input to construct a ⒸUser Representation. Employ ⒹAdaptation Assignmentlogic to identify when and how to introduce ⒺInterventions. Our novel taxonomy provides a road map for work in this area, describing the rich space of current approaches and highlighting open areas for future work. 

Abstract Photochemical C−C coupling reactions can be tailored to industrial chemical processes and preparations of pharmaceuticals. Recent approaches in this area are limited to using precious transition metal coordination complexes that facilitate light absorption and redox processes with benchtop chemicals. Herein, we propose a paradigm that involves all‐in‐one organo‐photo‐auxiliaries,thio‐heteroarenes, which exhibit unique photophysical properties. Thesethio‐heteroarenes were employed to prepare several all‐in‐one ionic photo‐salts from commercially available alkyl/benzyl and heterocyclic halides via aromaticity‐mediated nucleophilic substitution reactions. From the library of >30 salts, we performed on‐demand photochemical C−C coupling reactions to isolate numerous symmetrical and unsymmetrical diary/alkyl‐ethanes with yields up to 84% and mass balance as high as 96%. We also investigated the influence of structural features/properties on the outcomes of the photochemical C−C coupling reactions. The current photochemical C−C method was successful in the isolation of >30 photoproducts, including the natural product Brittonin A, a precursor of Imipramine, and derivatives of the bioactive Honokiol Analogues. Furthermore, transient absorption spectroscopy and time‐dependent density functional theory calculations were used to decipher the nature of light‐promoted electronic transitions. 

The prediction of crystal properties plays a crucial role in materials science and applications. Current methods for predicting crystal properties focus on modeling crystal structures using graph neural networks (GNNs). However, accurately modeling the complex interactions between atoms and molecules within a crystal remains a challenge. Surprisingly, predicting crystal properties from crystal text descriptions is understudied, despite the rich information and expressiveness that text data offer. In this paper, we develop and make public a benchmark dataset (TextEdge) that contains crystal text descriptions with their properties. We then propose LLM-Prop, a method that leverages the general-purpose learning capabilities of large language models (LLMs) to predict properties of crystals from their text descriptions. LLM-Prop outperforms the current state-of-the-art GNN-based methods by approximately 8% on predicting band gap, 3% on classifying whether the band gap is direct or indirect, and 65% on predicting unit cell volume, and yields comparable performance on predicting formation energy per atom, energy per atom, and energy above hull. LLM-Prop also outperforms the fine-tuned MatBERT, a domain-specific pre-trained BERT model, despite having 3 times fewer parameters. We further fine-tune the LLM-Prop model directly on CIF files and condensed structure information generated by Robocrystallographer and found that LLM-Prop fine-tuned on text descriptions provides a better performance on average. Our empirical results highlight the importance of having a natural language input to LLMs to accurately predict crystal properties and the current inability of GNNs to capture information pertaining to space group symmetry and Wyckoff sites for accurate crystal property prediction. 

Orbital-free density functional theory (OFDFT) enables full quantum-mechanical simulations based solely on electron densities but is limited by the lack of accurate kinetic-energy functionals. We improve upon the APBEK functional by tuning its μ parameter for a given system during density initialization and adding two non-empirical corrections based on Kato’s cusp condition and the virial theorem. The resulting functional, MuAPBEK, assessed on atoms and molecules, shows significantly lower energy errors than standard APBEK and Thomas–Fermi–von-Weizsäcker functionals, even when evaluated on converged Kohn–Sham density functional theory (KSDFT) densities. MuAPBEK also produces accurate densities that slightly deviate from those of KSDFT. Its density optimization step is over ten times faster than a single KSDFT SCF cycle and scales as O(N1.96), indicating that accurate, large-scale OFDFT simulations are feasible beyond practical KSDFT limits. 

The suppression of heat transport in disordered crystals arises from a competition between mass fluctuations and bond disorder, but their relative contributions remain difficult to disentangle.