[upstream-sync] README.md from ai-boost/awesome-ai-for-science@9cecfd73 [catalogue]

upstream/ai-boost-awesome-ai-for-science/catalogue/README.md

@@ -2,9 +2,9 @@
 title: "Readme"
 task: ""
 lineage_type: import
-upstream_source: https://github.com/ai-boost/awesome-ai-for-science/blob/024f42bf/README.md
-upstream_sha: 024f42bf
-imported_at: 2026-08-09
+upstream_source: https://github.com/ai-boost/awesome-ai-for-science/blob/9cecfd73/README.md
+upstream_sha: 9cecfd73
+imported_at: 2026-08-16
 prompt_class: catalogue
 upstream_changes: accepted
 author: upstream
@@ -63,6 +63,7 @@ validated: false
   - [🌍 Earth & Climate Science](#-earth--climate-science)
   - [🌾 Agriculture & Ecology](#-agriculture--ecology)
   - [🧠 Social Sciences](#-social-sciences)
+  - [🏗 Engineering & Built Environment](#-engineering--built-environment)
 - [🤖 Foundation Models for Science](#-foundation-models-for-science)
 - [📈 Datasets & Benchmarks](#-datasets--benchmarks)
 - [💻 Computing Frameworks](#-computing-frameworks)
@@ -328,6 +329,7 @@ validated: false
 ### Domain-Specific Research Agents
 - [Aletheia](https://arxiv.org/abs/2602.10177) - Google DeepMind's autonomous mathematics research agent powered by Gemini Deep Think, autonomously solving 4 open problems from 700 Erdős conjectures and generating complete research papers without human intervention (February 2026)
 - [AlphaProof Nexus (Google DeepMind, arXiv 2026)](https://github.com/google-deepmind/alphaproof-nexus-results) - LLM-driven formal proof search system that pairs large language models with Lean verification to solve open mathematics problems; autonomously resolved 9 of 353 Erdős problems and 44 of 492 OEIS conjectures, with proofs and natural-language prose released for combinatorics, optimization, graph theory, algebraic geometry, and quantum optics collaborations (282+ stars, Apache 2.0)
+- [Ten Proofs (OpenAI, 2026)](https://github.com/openai/ten-proofs) - Lean 4 formalizations of ten major advances in mathematics and theoretical computer science, including improved sphere-packing bounds, non-sofic groups, a counterexample to Connes's rigidity conjecture, and quantum parallel repetition; released with the OpenAI paper and reasoning walkthroughs (57+ stars, Apache 2.0)
 - [AlphaGeometry](https://github.com/google-deepmind/alphageometry) - DeepMind's Olympiad-level geometry theorem prover combining neural language model with symbolic deduction engine, AlphaGeometry2 solves 84% of IMO geometry problems (42/50) at gold-medalist level (Nature 2024)
 - [Goedel-Prover-V2](https://github.com/Goedel-LM/Goedel-Prover-V2) - Strongest open-source automated theorem prover in Lean 4, 8B model matches DeepSeek-Prover-V2-671B at 84.6% MiniF2F, 32B model achieves 90.4% with self-correction, using scaffolded data synthesis and verifier-guided proof refinement (Princeton, 2025)
 - [DeepSeek-Prover-V2](https://github.com/deepseek-ai/DeepSeek-Prover-V2) - DeepSeek's open-source large language model for formal theorem proving in Lean 4, integrating informal and formal mathematical reasoning through recursive subgoal decomposition and reinforcement learning powered by DeepSeek-V3, with open weights and ProverBench evaluation (2025)
@@ -638,6 +640,7 @@ validated: false
 - [SpikeInterface](https://github.com/SpikeInterface/spikeinterface) - Unified Python framework for extracellular electrophysiology, standardizing interfaces to 10+ ML-based spike sorting algorithms including Kilosort for reproducible neural spike sorting workflows (792+ stars, actively maintained)
 - [CaImAn (Flatiron Institute)](https://github.com/flatironinstitute/CaImAn) - Computational toolbox for large scale Calcium Imaging Analysis, including movie handling, motion correction, source extraction, spike deconvolution and result visualization, using machine learning for automated neuron detection and activity inference in two-photon and one-photon calcium imaging data (723+ stars, actively maintained)
 - [TRIBE v2](https://github.com/facebookresearch/tribev2) - Meta FAIR's foundation model of vision, audition, and language for in-silico neuroscience, predicting fMRI brain responses to naturalistic multimodal stimuli (video, audio, text) through unified Transformer architecture mapped to the cortical surface (2026)
+- [Brain2Qwerty (Meta FAIR, Nature Neuroscience 2026)](https://github.com/facebookresearch/brain2qwerty) - Non-invasive decoding of typed sentences from MEG and EEG brain recordings using a convolutional encoder, transformer, and character-level language model; official code for the Nature Neuroscience paper and Meta blog post on brain-AI communication (Meta FAIR, 894+ stars, CC BY-NC 4.0, 2026)
 - [braindecode](https://github.com/braindecode/braindecode) - Deep learning software to decode EEG, ECG or MEG signals, providing standardized neural network models, preprocessing pipelines, and evaluation workflows for brain-computer interfaces and cognitive neuroscience research (1.2K+ stars, BSD 3-Clause, actively maintained)
 - [snntorch](https://github.com/jeshraghian/snntorch) - Deep learning with spiking neural networks in Python, providing gradient-based training of SNNs via PyTorch autodifferentiation for brain-inspired computing and neuromorphic research, with online learning capabilities and extensive tutorials (1.9K+ stars, actively maintained)
 - [nilearn](https://github.com/nilearn/nilearn) - Machine learning and statistical learning for neuroimaging in Python, providing easy-to-use tools for fMRI and MRI analysis including decoding, connectivity estimation, and parcellation with seamless scikit-learn integration (INRIA Parietal team, 1.4K+ stars)
@@ -726,6 +729,7 @@ validated: false
 
 #### 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
 
@@ -741,6 +745,7 @@ validated: false
 - [TORAX](https://github.com/google-deepmind/torax) - Differentiable tokamak core transport simulator for fusion energy research, coupling PDE solvers with JAX auto-differentiation and neural-network surrogates for fast forward modelling, pulse-design, and trajectory optimization (Google DeepMind, Apache 2.0)
 - [DiffPhysDrone (Nature Machine Intelligence 2025)](https://github.com/HenryHuYu/DiffPhysDrone) - First real quadrotor robot trained end-to-end with differentiable physics for vision-based agile flight, bridging simulation-based learning and real-world deployment with physics-informed neural network controllers (558+ stars)
 - [Walrus (arXiv 2025)](https://github.com/PolymathicAI/walrus) - Cross-domain foundation model for continuum dynamics trained on 19 physical scenarios spanning 63 variables, featuring adaptive compute via stride modulation and patch jittering for long-run stability (Polymathic AI, 293+ stars, MIT License)
+- [GeoPT (ICML 2026)](https://github.com/Physics-Scaling/GeoPT) - Unified pre-trained model for general physics simulation via lifted geometric pre-training, augmenting static geometry with synthetic dynamics to enable dynamics-aware self-supervision without physics labels; improves industrial-fidelity benchmarks spanning fluid mechanics and solid mechanics while reducing labeled data requirements by 20–60% (Physics-Scaling, 224+ stars)
 
 #### Astronomy & Astrophysics
 - [AstroCLIP](https://github.com/PolymathicAI/AstroCLIP) - Cross-modal self-supervised foundation model for galaxies by Polymathic AI, jointly embedding multi-band galaxy imaging and optical spectra into a shared latent space to enable zero/few-shot redshift estimation, galaxy property prediction, morphology classification, and cross-modal similarity search (MNRAS Letters 2024)
@@ -785,6 +790,7 @@ validated: false
 - [SkySensePlusPlus](https://github.com/kang-wu/SkySensePlusPlus) - Semantic-enhanced multi-modal remote sensing foundation model for Earth observation (Nature Machine Intelligence 2025), enabling universal interpretation across diverse satellite imagery modalities with open-source weights and benchmarks
 - [TESSERA (CVPR 2026)](https://github.com/ucam-eo/tessera) - University of Cambridge's foundation model for time-series satellite imagery, enabling efficient extraction of temporal patterns from Earth observation for land classification, canopy height prediction, and other remote sensing tasks
 - [TerraMind (IBM & ESA, 2025)](https://github.com/IBM/terramind) - First any-to-any generative foundation model for Earth Observation, enabling unified multimodal understanding and generation across diverse satellite sensors and geospatial tasks through a single architecture (258+ stars)
+- [GeoAgent (opengeos, 2026)](https://github.com/opengeos/GeoAgent) - Shared multimodal AI agent layer for geospatial Python packages (leafmap, geoai, geemap, STAC, NASA Earthdata) and QGIS, exposing geospatial tools to LLMs with structured metadata, confirmation hooks, and support for OpenAI, Anthropic, Google Gemini, Ollama, and more; includes the OpenGeoAgent QGIS plugin (456+ stars, MIT License)
 - [Awesome Remote Sensing Foundation Models](https://github.com/Jack-bo1220/Awesome-Remote-Sensing-Foundation-Models) - Curated collection of papers, datasets, benchmarks, code, and pre-trained weights for Remote Sensing Foundation Models (RSFMs), tracking the rapidly evolving landscape of vision, vision-language, generative, and agent-based geospatial AI (1.9K+ stars, 2024-2026)
 
 ### 🌾 Agriculture & Ecology
@@ -813,6 +819,16 @@ validated: false
 
 ---
 
+## 🏗 Engineering & Built Environment
+
+### 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)
+
+---
+
 ## 🤖 Foundation Models for Science
 
 ### General Science Models
@@ -983,6 +999,7 @@ This project builds upon and complements several excellent resources:
 
 ### 📊 Paper & Research Collections
 - [Scientific LLM Papers](https://github.com/yuzhimanhua/Awesome-Scientific-Language-Models) - 260+ scientific language models
+- [Awesome Scientific LLM Benchmarks](https://github.com/subinium/Awesome-Scientific-LLM-Benchmarks) - Curated, accuracy-first collection of benchmarks for evaluating LLMs on scientific reasoning and discovery across mathematics, physics, chemistry, materials science, biology, and agentic science (subinium, 29+ stars, MIT License, 2026)
 - [LLM4SR Repository](https://github.com/du-nlp-lab/LLM4SR) - LLM for scientific research survey materials
 - [PINNs Paper Collection](https://github.com/idrl-lab/PINNpapers) - Physics-informed neural networks research
 - [SciML Papers](https://sciml.ai/papers/) - Scientific computing and machine learning papers