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Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To explore these possibilities, we organized a hackathon. This article chronicles the projects built as part of this hackathon. Participants employed LLMs for various applications, including predicting properties of molecules and materials, designing novel interfaces for tools, extracting knowledge from unstructured data, and developing new educational applications. The diverse topics and the fact that working prototypes could be generated in less than two days highlight that LLMs will profoundly impact the future of our fields. The rich collection of ideas and projects also indicates that the applications of LLMs are not limited to materials science and chemistry but offer potential benefits to a wide range of scientific disciplines.
Time resolved in situ (TRIS) monitoring has revolutionised the study of mechanochemical transformations but has been limited by available data quality. Here we report how a combination of miniaturised grinding jars together with innovations in X-ray powder diffraction data collection and state-of-the-art analysis strategies transform the power of TRIS synchrotron mechanochemical experiments. Accurate phase compositions, comparable to those obtained by ex situ measurements, can be obtained with small sample loadings. Moreover, microstructural parameters (crystal size and microstrain) can be also determined with high confidence. This strategy applies to all chemistries, is readily implemented, and yields high-quality diffraction data even using a low energy synchrotron source. This offers a direct avenue towards the mechanochemical investigation of reactions comprising scarce, expensive, or toxic compounds. Our strategy is applied to model systems, including inorganic, metal-organic, and organic mechanosyntheses, resolves previously misinterpreted mechanisms in mechanochemical syntheses, and promises broad, new directions for mechanochemical research.
Indirect combustion noise due to the interaction of flow inhomogeneities with a choked combustion-chamber exit is an important cause of combustion instability in solid rocket motors.
Moreover, it is believed to be an issue in electrical-power generation turbines and aero-engines. If these flow inhomogeneities are essentially characterized by the fluid having a locally appreciablydifferent thermodynamic state, the acoustic response engendered by its interaction with the combustion-chamber exit is commonly referred to as entropy noise. In this paper, dedicated numerical-simulation results of entropy-patch choked-nozzle interactions are presented. Two types of entropy patches were considered: rectangular slugs and circular spots. Moreover, analytical-model-based analysis, of said simulation results, is presented. Based on said analysis, the authors posit the existence of three modeling regimes: the quasi-steady-modeling regime, the blended-physical-effects regime, and the inertial-modeling regime.