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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. 

Abstract Graph neural networks (GNNs) have excelled in predictive modeling for both crystals and molecules, owing to the expressiveness of graph representations. High-entropy alloys (HEAs), however, lack chemical long-range order, limiting the applicability of current graph representations. To overcome this challenge, we propose a representation of HEAs as a collection of local environment graphs. Based on this representation, we introduce the LESets machine learning model, an accurate, interpretable GNN for HEA property prediction. We demonstrate the accuracy of LESets in modeling the mechanical properties of quaternary HEAs. Through analyses and interpretation, we further extract insights into the modeling and design of HEAs. In a broader sense, LESets extends the potential applicability of GNNs to disordered materials with combinatorial complexity formed by diverse constituents and their flexible configurations.