ComProScanner: AI That Reads 100 Papers in Seconds (Secret Tool) #Shorts

🚀 Ready to turn 100+ scientific papers into structured materials data in seconds? Meet *ComProScanner* — the revolutionary multi-agent AI framework that automates extraction of complex chemical compositions, piezoelectric coefficients (d₃₃), synthesis methods, precursors, and more — straight from unstructured literature! In this video, you’ll learn how ComProScanner uses *CrewAI + LLM agents with Retrieval-Augmented Generation (RAG)* to intelligently filter relevant papers, split extraction tasks across specialized agents (composition, synthesis), and even parse tricky variable formulas like Pb₁₋ₓKₓNb₂O₆ using a custom **deep learning parser**. We test it across *10 LLMs**, and DeepSeek-V3-0324 leads with **82% accuracy* — beating GPT-4.1-Nano and newer Gemini models! Plus, it evaluates results with semantic & agentic validation, normalizes data across papers, and generates **bar charts, heatmaps, and interactive knowledge graphs via Neo4j**. Best part? *20 lines of Python* to deploy it. Supports *TDM APIs from Elsevier, Springer, Wiley, and IOP* — no manual PDF downloads needed. Fully *open-source (MIT)* and available on **PyPI**. This is more than automation — it’s **AI-powered materials discovery at scale**. 👉 Like, Subscribe & Comment: “Materials AI” if you’re ready to revolutionize research! #MaterialsScience #AIResearch #ChemicalData #LLM #CrewAI #DeepLearning #Python #OpenSource #ScientificAI #DataExtraction #Shorts Read more on arxiv by searching for this paper: 2510.20362.pdf