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AI for science

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.

Here are 293 public repositories matching this topic...

open-science

AIPOCH Open-Science is an open-source, local-first, model-agnostic AI research workbench for macOS, Windows, and Linux, with scientific agents, Python/R notebooks, data connectors, and reproducible provenance.

  • Updated Sep 11, 2026
  • TypeScript

Open Science Desktop — local-first, model-agnostic AI research workbench for macOS, Windows & Linux. Open-source Claude Science desktop alternative built on Tauri + MCP + agent skills.

  • Updated Sep 11, 2026
  • TypeScript

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.

  • Updated Sep 11, 2026
  • Rust