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High-performance computing is a driving force behind scientific innovation and discovery. However, as the number of users and the complexity of high-performance computing systems grow, so does the volume and variability of technical issues handled by sup- port teams. The evolving nature of these issues presents a need for automated tools that can extract clear, accurate, and relevant fre- quently asked questions directly from support tickets. This need was addressed by developing a novel pipeline that incorporates seman- tic clustering, representation learning, and large language models. While prior research laid strong foundations across classification, clustering and large language model-based questions & answers, our work augments these efforts by integrating semantic clustering, domain-specific summarization, and multi-stage generation into a scalable pipeline for autonomous technical support. To prioritize high-impact issues, the pipeline began by filtering tickets based on anomaly frequency and recency. It then leveraged an instruction- tuned large language model to clean and summarize each ticket into a structured issue-resolution pair. Next, unsupervised semantic clus- tering was performed to identify subclusters of semantically similar tickets within broader topic clusters. A large language model-based generation module was then applied to create frequently asked questions representing the most dominant issues. A structured evaluation by subject matter experts indicated that our approach transformed technical support tickets into understandable, factu- ally sound, and pertinent frequently asked questions. The ability to extract fine-grained insights from raw ticket data enhances the scalability, efficiency, and responsiveness of technical support work- flows in high-performance computing environments, ultimately enabling faster troubleshooting and more accessible pathways to scientific discovery. 

Lithium–sulfur (Li–S) batteries have great potential as next generation energy storage devices. However, the redox chemistry mechanism involves the generation of solubilized lithium polysulfides, which can lead to leaching of the active material and, consequently, passivated electrodes and diminished capacities. Chemical tethering of lithium polysulfides to materials in the sulfur cathode is a promising approach for resolving this issue in Li–S batteries. Borrowing from the field of synthetic chemistry, we utilize maleimide functional groups in a Zr-based metal–organic framework to chemically interact with polysulfides through the Michael Addition reaction. A combination of molecular and solid-state spectroscopies confirms covalent attachment of Li 2 S x to the maleimide functionality. When integrated into Li–S cathodes, the maleimide-functionalized framework exhibits notable performance enhancements over that of the unfunctionalized material, revealing the promise of polysulfide anchors for Li–S battery cycling.