The all‐inorganic nature of CsPbI3 perovskites allows to enhance stability in perovskite devices. Research efforts have led to improved stability of the black phase in CsPbI3 films; however, these strategies—including strain and doping—are based on organic‐ligand‐capped perovskites, which prevent perovskites from forming the close‐packed quantum dot (QD) solids necessary to achieve high charge and thermal transport. We developed an inorganic ligand exchange that leads to CsPbI3 QD films with superior phase stability and increased thermal transport. The atomic‐ligand‐exchanged QD films, once mechanically coupled, exhibit improved phase stability, and we link this to distributing strain across the film. Operando measurements of the temperature of the LEDs indicate that KI‐exchanged QD films exhibit increased thermal transport compared to controls that rely on organic ligands. The LEDs exhibit a maximum EQE of 23 % with an electroluminescence emission centered at 640 nm (FWHM: ≈31 nm). These red LEDs provide an operating half‐lifetime of 10 h (luminance of 200 cd m−2) and an operating stability that is 6× higher than that of control devices.
The multi-agent pathfinding problem (MAPF) is the fundamental problem of planning paths for multiple agents, where the key constraint is that the agents will be able to follow these paths concurrently without colliding with each other. Applications of MAPF include automated warehouses, autonomous vehicles, and robotics. Research on MAPF has been flourishing in the past couple of years. Different MAPF research papers assume different sets of assumptions, e.g., whether agents can traverse the same road at the same time, and have different objective functions, e.g., minimize makespan or sum of agents' actions costs. These assumptions and objectives are sometimes implicitly assumed or described informally. This makes it difficult for establishing appropriate baselines for comparison in research papers, as well as making it difficult for practitioners to find the papers relevant to their concrete application. This paper aims to fill this gap and facilitate future research and practitioners by providing a unifying terminology for describing the common MAPF assumptions and objectives. In addition, we also provide pointers to two MAPF benchmarks. In particular, we introduce a new grid-based benchmark for MAPF, and demonstrate experimentally that it poses a challenge to contemporary MAPF algorithms.
We study prioritized planning for Multi-Agent Path Finding (MAPF). Existing prioritized MAPF algorithms depend on rule-of-thumb heuristics and random assignment to determine a fixed total priority ordering of all agents a priori. We instead explore the space of all possible partial priority orderings as part of a novel systematic and conflict-driven combinatorial search framework. In a variety of empirical comparisons, we demonstrate state-of-the-art solution qualities and success rates, often with similar runtimes to existing algorithms. We also develop new theoretical results that explore the limitations of prioritized planning, in terms of completeness and optimality, for the first time.
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