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We report the two-dimensional (2D) bimetallic selenophosphate, LiGaP2Se6, prepared through direct combination reactions and P2Se5 flux methods. The material is a member of the broad class of van der Waals 2D materials of the type M2P2Q6 (M = metals). The structure was determined using single-crystal X-ray diffraction and refined in the chiral space group P3̅1c, with lattice parameters a = b = 6.2993(9) Å, c = 13.308(3) Å, α = β = 90°, γ = 120°. Differential thermal analysis indicated a congruent melting point at ∼458 °C. Optoelectronic properties were assessed using ultraviolet–visible (UV–vis) spectroscopy, showing a band gap of 2.01 eV, and photoemission yield spectroscopy in air (PYSA), which determined a work function of 5.44 eV. Notably, stability studies on LiGaP2Se6 revealed remarkable resilience despite its Li content, showing no structural changes after 2 weeks in ambient air or after soaking in a water/ethanol bath. 

A revised crystal structure of La(OH)2Cl is reported. This material is found to crystallize in space group P21/m and is isostructural to a series of Ln(OH)2Cl (Ln = Ce – Lu excluding Pm). The Ln(OH)2Cl series has been thoroughly studied, serving as analogues to proposed actinide structures for used nuclear fuel storage. The P21/m space group has been reported for each isostructural variant in this series. La(OH)2Cl is described in the context of the structural trends identified with this series. A lanthanum variant was previously reported, however, with symmetry corresponding to the space group P2/m. The data collected herein is compared to the previously published La(OH)2Cl in the space group P2/m. Here, we report an updated hydrothermal synthesis and revised crystallographic structure for La(OH)2Cl in P21/m. The reflection conditions of the collected X‐ray diffraction data, the bond valence sums of both structures, and density functional theory calculations are examined to justify the revised space group assignments. 

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