[upstream-sync] README.md from ai-boost/awesome-ai-for-science@7f00e2f0 [catalogue]

2026-08-18 22:28:02 +00:00
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 title: "Readme"
 task: ""
 lineage_type: import
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-imported_at: 2026-08-14
+upstream_source: https://github.com/ai-boost/awesome-ai-for-science/blob/7f00e2f0/README.md
+upstream_sha: 7f00e2f0
+imported_at: 2026-08-18
 prompt_class: catalogue
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 author: upstream
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 - [LeanDojo](https://github.com/lean-dojo/LeanDojo) - Open-source toolkit and benchmark for learning-based theorem proving in Lean, providing programmatic Lean interaction, a 98K+ theorem dataset extracted from 217 Lean projects, and ReProver—the first retrieval-augmented LLM-based theorem prover for Lean—with reproducible training pipelines underpinning much subsequent Lean prover research (Caltech & NVIDIA, NeurIPS 2023 Outstanding Paper, Datasets & Benchmarks)
 - [Lean Copilot](https://github.com/lean-dojo/LeanCopilot) - LLMs as copilots for theorem proving in Lean 4, exposing native tactics (`suggest_tactics`, `search_proof`, `select_premises`) that embed language model inference and premise retrieval directly inside the Lean proof environment, supporting local CTranslate2/CUDA inference as well as remote model APIs for interactive and automated proof search (Caltech & NVIDIA, NeurIPS 2024, 1.2K+ stars)
 - [MathCode](https://github.com/math-ai-org/mathcode) - Terminal AI coding assistant with a built-in math formalization engine that converts plain-language math problems into Lean 4 theorems and attempts formal proofs; bundles a local Lean toolchain and WebUI for interactive mathematical reasoning (math-ai-org, 582+ stars, 2026)
+- [TorchLean (lean-dojo, 2026)](https://github.com/lean-dojo/TorchLean) - First unified Lean 4 framework for neural-network specification, execution, and verification; tensor shapes are part of the types, models are executable Lean programs, and the same definitions can be used by training code, graph transformations, certificate checkers, and proofs with CPU/CUDA backends (123+ stars, MIT License)
 - [Get Physics Done (PSI)](https://github.com/psi-oss/get-physics-done) - First open-source agentic AI physicist turning research questions into structured workflows with rigorous verification and multi-step analytical work for long-horizon physics projects; integrates with Claude Code, Codex, Gemini CLI, and OpenCode (804+ stars, Apache 2.0, 2026)
 - [Foam-Agent (NeurIPS 2025)](https://github.com/csml-rpi/Foam-Agent) - End-to-end composable multi-agent framework for automating OpenFOAM-based CFD simulations from natural language prompts, managing meshing, case setup, execution, error correction, and post-processing; achieves 100% success rate on 110 FoamBench tasks with Claude Opus 4.6 through Architect-Input Writer-Runner-Reviewer agent collaboration with RAG-enhanced generation and MCP tool integration (RPI CSML, 242+ stars, MIT License)
+- [AI CFD Scientist (RPI CSML, arXiv 2026)](https://github.com/csml-rpi/AI-CFD-Scientist) - Open-ended AI scientist for computational fluid dynamics that spans literature-grounded ideation, OpenFOAM execution via Foam-Agent, vision-language physics verification of rendered flow fields, source-code modification for new physical models, and figure-grounded LaTeX manuscript writing within a single inspectable workflow (43+ stars, Python)
 - [Zephyrus (ICLR 2026)](https://github.com/Rose-STL-Lab/Zephyrus) - First agentic framework for weather science, pairing an LLM with ZephyrusWorld (a code-execution environment exposing WeatherBench 2 data, geolocation, forecasting, simulation, and climatology tools) and ZephyrusBench (2,230 Q&A pairs across 49 weather-science tasks); outperforms text-only baselines by up to 44.2 percentage points (UC San Diego Rose-STL-Lab, 99+ stars, MIT License, 2026)
 - [BioDiscoveryAgent](https://github.com/snap-stanford/BioDiscoveryAgent) - AI agent for biological discovery and research automation
 - [Biomni](https://github.com/snap-stanford/Biomni) - General-purpose biomedical AI agent integrating LLM reasoning with retrieval-augmented planning and code-based execution to autonomously execute diverse biomedical research tasks and generate testable hypotheses (Stanford SNAP, bioRxiv 2025)
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 #### Lab Automation & Robotics
 - [PyLabRobot](https://github.com/PyLabRobot/pylabrobot) - Interactive and hardware-agnostic SDK for laboratory automation, enabling programmatic control of liquid handlers, plate readers, and other lab instruments across multiple vendors; foundational infrastructure for self-driving laboratories and AI-driven experimental execution (447+ stars)
+- [RoboChem-Flex](https://github.com/Noel-Research-Group/Robochem_Flex) - Low-cost, modular self-driving laboratory platform democratizing autonomous chemical experimentation with open control software, device CAD/PCB files, and example optimization campaigns (Noël Research Group, University of Amsterdam, Apache 2.0, 2026)

 ### 🌌 Physics & Astronomy

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 ### Structural & Civil Engineering
 - [StructureClaw](https://github.com/structureclaw/structureclaw) - AI-assisted structural engineering workspace for AEC workflows: natural language to structural model, analysis, code-check, and report (171+ stars, MIT License, 2026)

+### Architectural Design & BIM
+- [Aedifex](https://github.com/TangSY/aedifex) - Open-source 3D architectural editor with an AI design assistant; build floor plans with walls, doors, windows, and furniture using natural language, with real-time WebGPU-powered previews (TangSY, 59+ stars, MIT License, 2026)
+
+### Electrical & Electronics Engineering
+- [kicad-happy](https://github.com/aklofas/kicad-happy) - AI coding agent skills for KiCad electronics design that turn Claude Code, Codex, Gemini CLI, and other coding agents into full electronics design assistants; parses schematics and PCB layouts, builds power trees, audits connectors/ESD protection, validates passive networks, runs SPICE simulation, sources components from major distributors, and prepares boards for fabrication (aklofas, 974+ stars, MIT License, 2026)
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 ## 🤖 Foundation Models for Science