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We synthesized single crystals for Mn2-xZnxSb (0 ≤ x ≤ 1) and studied their magnetic and electronic transport properties. This material system displays rich magnetic phase tunable with temperature and Zn composition. In addition, two groups of distinct magnetic and electronic properties, separated by a critical Zn composition of x = 0.6, are discovered. The Zn-less samples are metallic and characterized by a resistivity jump at the magnetic ordering temperature, while the Zn-rich samples lose metallicity and show a metal-to-insulator transition-like feature tunable by magnetic field. Our findings establish Mn2-xZnxSb as a promising material platform that offers opportunities to study how the coupling of spin, charge, and lattice degrees of freedom governs interesting transport properties in 2D magnets, which is currently a topic of broad interest. 

In-time particle trajectory reconstruction in the Large Hadron Collider is challenging due to the high collision rate and numerous particle hits. Using GNN (Graph Neural Network) on FPGA has enabled superior accuracy with flexible trajectory classification. However, existing GNN architectures have inefficient resource usage and insufficient parallelism for edge classification. This paper introduces a resource-efficient GNN architecture on FPGAs for low latency particle tracking. The modular architecture facilitates design scalability to support large graphs. Leveraging the geometric properties of hit detectors further reduces graph complexity and resource usage. Our results on Xilinx UltraScale+ VU9P demonstrate 1625x and 1574x performance improvement over CPU and GPU respectively. 

Intercalation has become a powerful approach to tune the intrinsic properties and introduce novel phenomena in layered materials. Intercalating van der Waals (vdW) magnetic materials is a promising route to engineer the low-dimensional magnetism. Recently, metal thiophosphates,MPX₃, has been widely studied because their magnetic orders are highly tunable and persist down to the two-dimensional limit. In this work, we used electrochemical technique to intercalate Li into NiPS₃single crystals and found the emergence of ferrimagnetism at low temperature in Li-intercalated NiPS₃. Such tuning of magnetic properties highlights the effectiveness of intercalation, providing a novel strategy to manipulate the magnetism in vdW magnets.

 

Recent developments in 2D magnetic materials have motivated the search for new van der Waals magnetic materials, especially Ising‐type magnets with strong magnetic anisotropy. Fe‐basedMPX₃(M= transition metal,X= chalcogen) compounds such as FePS₃and FePSe₃both exhibit an Ising‐type magnetic order, but FePSe₃receives much less attention compared to FePS₃. This work focuses on establishing the strategy to engineer magnetic anisotropy and exchange interactions in this less‐explored compound. Through chalcogen and metal substitutions, the magnetic anisotropy is found to be immune against S substitution for Se whereas tunable only with heavy Mn substitution for Fe. In particular, Mn substitution leads to a continuous rotation of magnetic moments from the out‐of‐plane direction toward the in‐plane. Furthermore, the magnetic ordering temperature displays non‐monotonic doping dependence for both chalcogen and metal substitutions but due to different mechanisms. These findings provide deeper insight into the Ising‐type magnetism in this important van der Waals material, shedding light on the study of other Ising‐type magnetic systems as well as discovering novel 2D magnets for potential applications in spintronics.

 

The determination of charged particle trajectories in collisions at the CERN Large Hadron Collider (LHC) is an important but challenging problem, especially in the high interaction density conditions expected during the future high-luminosity phase of the LHC (HL-LHC). Graph neural networks (GNNs) are a type of geometric deep learning algorithm that has successfully been applied to this task by embedding tracker data as a graph—nodes represent hits, while edges represent possible track segments—and classifying the edges as true or fake track segments. However, their study in hardware- or software-based trigger applications has been limited due to their large computational cost. In this paper, we introduce an automated translation workflow, integrated into a broader tool called hls4ml , for converting GNNs into firmware for field-programmable gate arrays (FPGAs). We use this translation tool to implement GNNs for charged particle tracking, trained using the TrackML challenge dataset, on FPGAs with designs targeting different graph sizes, task complexites, and latency/throughput requirements. This work could enable the inclusion of charged particle tracking GNNs at the trigger level for HL-LHC experiments.