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Chalcogenide perovskites have attracted increasing research attention in recent years due to their promise of unique optoelectronic properties combined with stability. However, the synthesis and processing of these materials has been constrained by the need for high temperatures and/or long reaction times. In this work, we address the open question of a low-temperature growth mechanism for BaZrS3. Ultimately, a liquid-assisted growth mechanism for BaZrS3 using molten BaS3 as a flux is demonstrated at temperatures ≥540 °C in as little as 5 min. The role of Zr-precursor reactivity and S(g.) on the growth mechanism and the formation of Ba3Zr2S7 is discussed, in addition to the purification of resulting products using a straightforward H2O wash. The extension of this growth mechanism to other Ba-based chalcogenides is shown, including BaHfS3, BaNbS3, and BaTiS3. In addition, an alternative vapor-transport growth mechanism is presented using S2Cl2 for the growth of BaZrS3 at temperatures as low as 500 °C in at least 3 h. These results demonstrate the feasibility of scalable processing for the formation of chalcogenide perovskite thin-films. (DOI: 10.1021/acs.chemmater.3c00494) 

Coupled partial differential equations (PDEs) are key tasks in modeling the complex dynamics of many physical processes. Recently, neural operators have shown the ability to solve PDEs by learning the integral kernel directly in Fourier/Wavelet space, so the difficulty for solving the coupled PDEs depends on dealing with the coupled mappings between the functions. Towards this end, we propose a coupled multiwavelets neural operator (CMWNO) learning scheme by decoupling the coupled integral kernels during the multiwavelet decomposition and reconstruction procedures in the Wavelet space. The proposed model achieves significantly higher accuracy compared to previous learning-based solvers in solving the coupled PDEs including Gray-Scott (GS) equations and the non-local mean field game (MFG) problem. According to our experimental results, the proposed model exhibits a 2ˆ „ 4ˆ improvement relative L2 error compared to the best results from the state-of-the-art models.