TY - GEN A1 - Oehring, Michael A1 - Stark, Andreas A1 - Paul, Jonathan David Heaton A1 - Lippmann, Thomas A1 - Pyczak, Florian T1 - Microstructural refinement of boron-containing β-solidifying γ-titanium aluminide alloys through heat treatments in the β phase field T2 - Intermetallics Y1 - 2012 SN - 0966-9795 IS - 32 SP - 12 EP - 20 ER - TY - GEN A1 - Schnabel, Jan Eike A1 - Bargmann, Swantje A1 - Paul, Jonathan David Heaton A1 - Oehring, Michael A1 - Pyczak, Florian T1 - Work hardening and recovery in fully lamellar TiAl: relative activity of deformation systems T2 - Philosophical Magazine Y1 - 2019 SN - 1478-6435 SN - 1478-6443 VL - 99 IS - 2 SP - 148 EP - 180 ER - TY - GEN A1 - Wang, Li A1 - Liang, Xiaopeng A1 - Liu, Bin A1 - Oehring, Michael A1 - Paul, Jonathan David Heaton A1 - Liu, Jie A1 - Song, Min A1 - Pyczak, Florian A1 - Liu, Yong T1 - Stacking fault formation in perovskite Ti3AlC carbides in a TiAl based alloy during creep at 800°C T2 - Scripta Materialia Y1 - 2023 U6 - https://doi.org/10.1016/j.scriptamat.2022.115034 SN - 1872-8456 SN - 1359-6462 VL - Vol. 222 ER - TY - GEN A1 - Gupta, Vipul A1 - Paul, Jonathan David Heaton A1 - Schmitt, Ingo A1 - Pyczak, Florian T1 - SciLitMiner : an intelligent system for scientific literature mining and knowledge discovery T2 - Advanced intelligent systems N2 - Recent advances in data mining have enabled automation in literature‐based discovery (LBD), allowing synergistic evaluation of experimental findings reported in scientific publications. However, existing tools and digital libraries fall short in generating relevant literature collections and evaluating them for highly specific questions. This article presents SciLitMiner, an intelligent system to address this gap. SciLitMiner enables federated ingestion of literature from digital libraries; applies advanced retrieval techniques, including dataset‐aware retrieval from visual elements, to identify relevant studies; and leverages retrieval‐augmented generation (RAG) tailored to domain‐specific knowledge reasoning. The system is applied in materials science to study the creep behavior of γ‐TiAl alloys, revealing the intricate interplay between material, process, microstructure, and creep rate, represented through knowledge graphs. Two domain experts rate responses from the knowledge reasoning workflow with OpenAI large language models (LLMs) as the backbone above “good” (3 on a 5‐point Likert scale) in over 90% across qualitative criteria, indicating strong performance. In a case study, the workflow also outperforms gpt‐4.5‐turbo with web search and other leading tools in reliability. A second case study benchmarks open‐source LLMs as drop‐in replacements for proprietary models, demonstrating comparable‐to‐superior performance. The system's flexibility enables its use in automated LBD across diverse research domains. Y1 - 2025 U6 - https://doi.org/10.1002/aisy.202501235 SN - 2640-4567 SP - 1 EP - 20 PB - Wiley CY - Weinheim ER -