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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.
The ease with which an energetic material (explosives, propellants, and pyrotechnics) can be initiated is a critical parameter to assess their safety and application. Impact sensitivity parameters are traditionally derived experimentally, at great cost and risk to safety. In this work we explore a fully ab initio Approach based on concepts of vibrational energy transfer to predict impact sensitivities for a series of chemically, structurally and energetically diverse molecular materials. The quality of DFT calculations is assessed for a subset of the materials by comparison with experimental inelastic neutron scattering spectra (INS). A variety of models are considered, including both qualitative and quantitative analysis of the vibrational spectra. Excellent agreement against experimental impact sensitivity is achieved by consideration of a multi-phonon ladder-type up-pumping mechanism that includes both overtone and combination pathways, and is improved further by the added consideration of temperature. This fully ab initio approach not only permits ranking of energetic materials in terms of their impact sensitivity but also provides a tool to guide the targeted design of advanced energetic compounds with tailored properties.