Turn any PDF or image document into structured data for your AI. A powerful, lightweight OCR toolkit that bridges the gap between images/PDFs and LLMs. Supports 100+ languages.
-
Updated
Sep 16, 2026 - Python
AI for science is the application of machine learning and artificial intelligence methods to accelerate research and discovery across scientific domains. It encompasses work in protein structure prediction, climate modeling, drug discovery, materials design, and particle physics, among others.
Rather than replacing traditional scientific methods, AI for science augments them by learning patterns from experimental and simulation data to generate hypotheses, design experiments, and build fast surrogate models. Landmark examples include AlphaFold for protein structure prediction, GraphCast for weather forecasting, and FermiNet for quantum chemistry.
Turn any PDF or image document into structured data for your AI. A powerful, lightweight OCR toolkit that bridges the gap between images/PDFs and LLMs. Supports 100+ languages.
Transforms complex documents like PDFs and Office docs into LLM-ready markdown/JSON for your Agentic workflows.
🔬 Harness Vibe Research with Self-evolving AI Scientists
A blueprint-driven AutoResearch runtime for orchestrating AI research workflows from idea generation and experiments to paper writing and peer review.
Graphormer is a general-purpose deep learning backbone for molecular modeling.
Toward High-Accuracy Open-Source Biomolecular Structure Prediction.
A curated list of awesome AI tools, libraries, papers, datasets, and frameworks that accelerate scientific discovery — from physics and chemistry to biology, materials, and beyond.
GraphGen: Enhancing Supervised Fine-Tuning for LLMs with Knowledge-Driven Synthetic Data Generation
Open-source, local-first desktop AI research workbench for scientific computing with Python/R, MCP bioinformatics tools, SSH/WSL/GPU runtimes, and OpenAI/Anthropic models.
A Python toolkit for fine-tuning Geospatial Foundation Models (GFMs).
Principia extracts reusable principles, composes those principles into traceable research ideas, and helps researchers inspect why an idea may be worth testing.
🚀🚀🚀A collection of some awesome public projects about Large Language Model(LLM), Vision Language Model(VLM), Vision Language Action(VLA), AI Generated Content(AIGC), the related Datasets and Applications.
🏆 A ranked list of awesome atomistic machine learning projects ⚛️🧬💎.
freephdlabor: customizing personalized multiagent systems that researchs 24/7 on your own scientific problem
A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discovery (EMNLP'24)
[ECCV 2026] A diffusion-based framework for document OCR that replaces autoregressive decoding with block-level parallel diffusion decoding.
Terminal-Bench-Science: Evaluating AI agents on research workflows across scientific domains
MatterSim: A deep learning atomistic model across elements, temperatures and pressures.
9.9 元豆包 API 复刻 Claude Science
The State Layer of Agent Sandbox