AI is revolutionizing scientific research - from drug discovery and materials design to climate modeling and astrophysics. This repository collects the best resources to help researchers leverage AI in their work.
📚 Contents
🧪 AI Tools for Research
Literature & Knowledge Management
- Semantic Scholar - AI-powered academic search (Allen AI)
- arXiv - Open-access repository of electronic preprints and postprints
- OpenAlex - Open catalog of scholarly papers and authors
- CORE - Aggregator of open access research papers
- Connected Papers - AI-powered visual graph for exploring academic papers and discovering connected research through citation networks and semantic similarity
- PaSa (ByteDance) - Advanced paper search agent powered by large language models, autonomously invoking search tools, reading papers, and selecting references to deliver comprehensive and accurate results for complex scholarly queries (1.5K+ stars, Apache 2.0, 2024)
- paper-search-mcp - MCP server, CLI, and agent skills for searching and downloading academic papers from multiple open sources (arXiv, PubMed, bioRxiv, Semantic Scholar, OpenAlex, CORE, Europe PMC, etc.) with unified, deduplicated, LLM-friendly retrieval and an OA-first download fallback chain (OpenAGS, 1.9K+ stars, MIT License, 2025)
Data Analysis & Visualization
- PandasAI - Conversational data analysis using natural language
- DeepAnalyze - First agentic LLM for autonomous data science with end-to-end pipeline from data to analyst-grade reports
- AutoViz - Automated data visualization with minimal code
- Chat2Plot - Secure text-to-visualization through standardized chart specifications
Data Labeling & Annotation
- Label Studio - Multi-type data labeling and annotation tool
- Snorkel - Programmatic data labeling and weak supervision
Research Workbench & Plugins
- Claude Scientific Skills - Comprehensive collection of 125+ ready-to-use scientific skill modules for Claude AI across bioinformatics, cheminformatics, clinical research, ML, and materials science
- GDM Science Skills - Google DeepMind's official collection of agentic science skills accelerating scientific workflows with better grounding and higher token efficiency, integrating insights from AlphaGenome, AFDB, UniProt and 30+ other databases and tools (2026)
- Scientific Agent Skills - Turn any AI agent into an AI Scientist. The #1 Agent Skills library for science with 140+ ready-to-use skills and 100+ scientific databases covering biology, chemistry, medicine, and drug discovery. Compatible with Cursor, Claude Code, Codex, Antigravity, and the open Agent Skills standard (K-Dense-AI, 26K+ stars, 2025)
- SciAgent-Skills - 197 bioinformatics and life science skills for Claude Code and AI agents, achieving 92.0% accuracy on BixBench. Covers RNA-seq, single-cell analysis, drug discovery, proteomics, and more. Powers OmicsHorizon (195+ stars, 2026)
- Medical Research Skills - Curated library of 550+ medical research agent skills spanning evidence insights, protocol design, omics/clinical data analysis, and academic writing; each skill is reviewed through MedSkillAudit and compatible with Claude Code, Codex, Open Code, OpenClaw, and SKILL.md-compatible agents (AIPOCH, 1.2K+ stars, MIT License, 2026)
- bioSkills - Collection of SKILLS.md guiding AI coding agents (Claude Code, OpenAI Codex, Google Gemini, OpenCode, OpenClaw) through common bioinformatics workflows from basic sequence manipulation to advanced analyses such as single-cell RNA-seq and population genetics; evaluated on the Bio-Task Bench dataset (GPTomics, 969+ stars, MIT License, 2026)
📄 Paper→Poster / Slides / Graphical Abstract
Poster Generation
- Paper2Poster - Multi-agent system with Parser-Planner-Painter architecture converting
paper.pdf to editable poster.pptx, outperforms GPT-4o with 87% fewer tokens
- mPLUG-PaperOwl - Multimodal LLM for scientific charts and diagrams understanding/generation
Slides & Presentation Generation
- Auto-Slides - Multi-agent academic paper to high-quality presentation slides with interactive refinement
- PPTAgent - Beyond text-to-slides generation with PPTEval multi-dimensional evaluation (EMNLP 2025)
- paper2slides - Transform arXiv papers into Beamer slides using LLMs
- PaperToSlides - AI-powered tool that automatically converts academic papers (PDF) into presentation slides
- pdf2slides - Convert PDF files into editable slides with three lines of code
- SlideDeck AI - Co-create PowerPoint presentations with Generative AI from documents or topics
- AI Multi-Agent Presentation Builder - Azure Semantic Kernel multi-agent PPT generation reference
Video & Media Generation
- Paper2Video - First benchmark for automatic video generation from scientific papers (NeurIPS 2025)
- paper2video - Transform arXiv research papers into engaging presentations and YouTube-ready videos
Website & Interactive Content Generation
- Paper2All - AI-powered pipeline converting papers into interactive websites, posters, and multimedia presentations with "Let's Make Your Paper Alive!" philosophy
Figure & Illustration Generation
- PaperBanana - Automated academic illustration generation for AI scientists, converting research papers into publication-ready figures using VLMs and diffusion models with iterative refinement (PKU & Google Research, 6.2K+ stars, 2026)
Chart & Visualization Generation
Note: For comprehensive chart understanding and code generation tools, see 📊 Chart Understanding & Generation section
📊 Chart Understanding & Generation
Chart-to-Code & Reproducibility
Scientific Visualization Tools
- Chat2Plot - Secure text-to-visualization through standardized chart specifications
- AutoViz - Automated data visualization with minimal code
- PlotlyAI - AI-powered data visualization and dashboard creation
- Flint (Microsoft) - Visualization intermediate language that lets AI agents create expressive, polished charts from simple, human-editable specs, compiling the same input to 30+ chart types across Vega-Lite, ECharts, and Chart.js with an MCP server for agent integration (1.9K+ stars, MIT License, 2026)
🔄 Paper-to-Code & Reproducibility
Automated Code Generation
- Paper2Code - Automated code generation from machine learning research papers into runnable implementations (4.5K+ stars, 2025)
- Paper2Agent - Multi-agent system automatically transforming research papers into interactive AI agents with MCP server generation, tutorial auto-detection, and benchmark extraction (2.2K+ stars, MIT License, 2025)
- AutoP2C - LLM agent framework generating runnable repositories from academic papers
- ResearchCodeAgent - Multi-agent system for automated codification of research methodologies
- ToolMaker - Convert papers with code into callable agent tools
Experiment Automation
- BioProBench - Comprehensive benchmark for automatic evaluation of LLMs on biological protocols and procedural understanding
- Alhazen - Extract experimental metadata and protocol information from scientific documents
📋 Scientific Documentation & Parsing
High-Performance Document Processing
- MinerU (2024/2025) - SOTA multimodal document parsing with 1.2B parameters outperforming GPT-4o, converts PDFs to LLM-ready Markdown/JSON
- MinerU-Diffusion (OpenDataLab, ECCV 2026) - Diffusion-based document OCR framework replacing autoregressive decoding with block-level parallel diffusion decoding, enabling high-accuracy text recognition in scientific PDFs (613+ stars, MIT License)
- OpenDataLoader PDF (OpenDataLoader, 2025) - Open-source PDF parser for AI-ready data, converting PDFs into Markdown/JSON/HTML/Tagged PDF with layout analysis and reading-order detection; ranks #1 overall on extraction benchmarks with deterministic bounding boxes and hybrid AI mode (26K+ stars, Apache 2.0)
- PDF-Extract-Kit (2024) - Comprehensive toolkit for high-quality PDF content extraction with layout detection, formula recognition, and OCR
- Docling (IBM, AAAI 2025) - Multi-format (PDF/DOCX/PPTX/HTML/Images) → structured data (Markdown/JSON) with layout reconstruction, table/formula recovery
- Nougat (Meta AI) - Neural optical understanding for academic documents, transforms scientific PDFs to Markdown with mathematical formula support
- olmOCR (AllenAI) - Toolkit for linearizing academic PDFs into LLM-ready text with high accuracy and structure preservation, optimized for scientific literature extraction
- PaddleOCR 3.0 (2024/2025) - Advanced OCR with PP-StructureV3 document parsing, 13% accuracy improvement, supports 80+ languages
- Unstructured - Production-grade ETL for transforming complex documents into structured formats, with open-source API
- - High-accuracy PDF→Markdown/JSON/HTML conversion, specialized for tables/formulas/code blocks with benchmark scripts
Production Pipelines & Data Preparation
Figure & Table Extraction
- PDFFigures2 - Extract figures, tables, captions, and section titles from scholarly PDFs
- TableBank - Large-scale table detection and recognition dataset with pre-trained models
Scientific Text Processing & NLP
- scispacy (AllenAI) - Full spaCy pipeline and models for scientific/biomedical documents, enabling named entity recognition, abbreviation resolution, and UMLS linking for scientific literature mining (1.9K+ stars, Apache 2.0)
Scientific Literature RAG & Analysis
- PaperFold - ArXiv reader with five-level semantic zoom from overview down to verbatim excerpts, AI summaries, and paragraph-linked answers, folding papers, web pages, files, and AI replies into structured levels of detail (781+ stars, Apache 2.0, 2026)
- PaperQA2 - High-accuracy RAG for scientific PDFs with citation support, agentic RAG, and contradiction detection
- OpenScholar - Retrieval-augmented LM synthesizing scientific literature from 45M papers with human-expert-level citation accuracy, outperforming GPT-4o by 5% on ScholarQABench (Nature 2026, UW & Ai2)
- Valsci - Self-hostable scientific claim-verification and literature-review tool combining Semantic Scholar retrieval, bibliometric scoring, and LLM-based evidence synthesis for large-batch validation workflows
- paper-reviewer - Generate comprehensive reviews from arXiv papers and convert to blog posts
- STORM - LLM agent system synthesizing Wikipedia-like long-form research articles from scratch through multi-perspective question asking, web retrieval, and citation-grounded report generation, with Co-STORM extension for collaborative human-LLM knowledge curation conversations (Stanford OVAL, NAACL 2024 & EMNLP 2024)
🧰 Research Workbench & Plugins
Interactive Research Environments
- Jupyter AI (JupyterLab Extension) - Official Jupyter extension with
%%ai magic commands and sidebar chat assistant, connecting multiple model providers and local inference
- Notebook Intelligence (NBI) - AI coding assistant for JupyterLab with agent mode, supporting arbitrary LLM providers (2025+)
- Google Colab AI Features - Integrated AI assistance for data science and research notebooks
- OpenAI4S - Open-source hybrid scientific research agent and workbench replicating Claude Science, combining JSON tool orchestration with persistent Python/R Code-as-Action kernels, 604 bundled science skills, MCP connectors, sandboxed local execution, and multi-provider LLM support for end-to-end scientific workflows (PKU–YuanKong Intelligence, 377+ stars, MIT License, 2026)
- OpenBioMed - Open-source biomedical AI platform integrating multimodal foundation models (BioMedGPT, PharmolixFM, LangCell) with agentic workflows and 45+ Claude Code skills for drug discovery, protein engineering, and single-cell omics analysis (PharMolix & Tsinghua AIR, 1K+ stars, 2023-2026)
- AutoR - Human-centered research OS with terminal-first harness and local browser Studio, turning research work into reproducible artifact-backed runs through a 9-stage workflow with human approval gates, resume/rollback controls, and venue-aware manuscript packaging (1K+ stars, 2026)
- Dr. Claw (EMNLP 2026 System Demonstration) - Full-stack local AI research workspace spanning literature review, ideation, experiments, academic writing, and research artifact production (OpenLAIR, 1.2K+ stars, 2026)
- ScholarAIO - Agent-agnostic research infrastructure providing AI agents with a structured scientific workspace for deep PDF parsing, hybrid semantic/keyword literature search, citation-graph analysis, topic discovery, and academic writing workflows; natively integrates with Claude Code, Codex, Cursor, Cline, and AgentSkills.io (530+ stars, MIT License, 2026)
Literature Management Plugins
- llm-for-zotero - Research agent system deeply integrated with Zotero supporting Agent Mode, skills, multi-model backends (OpenAI-compatible, Claude Code, WebChat, Codex), and MinerU PDF parsing for literature Q&A, summarization, figure inspection, and source comparison (1.3K+ stars, 2026)
- PapersGPT for Zotero - Multi-PDF conversation, retrieval, and citation in Zotero with commercial/local models (Ollama), MCP support
- Zotero-GPT (MuiseDestiny) - Classic open-source plugin for document Q&A and summarization within Zotero
- Better BibTeX for Zotero - Enhanced citation key management and LaTeX integration
Scientific Writing & Collaboration
- Notion AI - AI-powered research note-taking and knowledge management
- Obsidian Smart Connections - AI-powered note linking and research graph navigation
- Research Rabbit - AI-powered literature discovery and research network mapping
- SciWrite - Agent skill for AI-assisted scientific manuscript writing review distilled from Stanford's Writing in the Sciences course, performing five sequential editorial audit passes on clarity, voice, structure, consistency, and integrity (2026)
- PaperSpine - Motivation-driven academic writing system for Claude Code, Codex, OpenClaw, and Hermes CLI that learns from strong papers, builds evidence-aware central-argument blueprints, and rewrites manuscripts with revision matrices and LaTeX-safe audits (4.9K+ stars, MIT License, 2026)
- Claude Prism - Offline-first scientific writing workspace powered by Claude, integrating LaTeX, Python, and 100+ scientific skills with local execution, Zotero integration, and privacy-focused design (2026)
- ScholarCopilot (COLM 2025) - Open-weight (Qwen2.5-7B) academic-writing assistant that jointly trains retrieval and generation, learning to emit a
[RET] token that fetches and inserts accurate citations from a 500K-paper corpus with one-click BibTeX; released with open data, model weights, and a live HuggingFace demo (TIGER-AI-Lab, 253+ stars, MIT License, 2025)
🕸 Knowledge Extraction & Scholarly KGs
Knowledge Graph Construction
- iText2KG - Incremental knowledge graph construction using LLMs with entity extraction and Neo4j visualization
- GraphGen - Knowledge graph-guided synthetic data generation for LLM fine-tuning, achieving strong performance on scientific QA (GPQA-Diamond) and math reasoning (AIME)
- KoPA - Structure-aware prefix adaptation for integrating LLMs with knowledge graphs (ACM MM 2024)
- Scholarly KGQA - LLM-powered question answering over scholarly knowledge graphs (ArXiv paper)
- SciAtlas - Large-scale knowledge graph and pip-installable client for literature-grounded automated scientific research, connecting papers, authors, institutions, venues, keywords, citations, and a four-level research taxonomy across medicine, social sciences, engineering, computer science, materials science, and more (ZJU NLP, arXiv 2026, 136+ stars, MIT License)
Knowledge Graph Resources
- Awesome-LLM-KG - Comprehensive collection of papers on unifying LLMs and knowledge graphs
🤖 Research Agents & Autonomous Workflows
Autonomous Research Systems (2023-2025 Breakthroughs)
- FunSearch (DeepMind, Nature 2023) - First system to make novel, verifiable scientific discoveries by pairing LLMs with evolutionary search, solving open problems in combinatorics (cap set problem) and discovering faster matrix multiplication algorithms
- AlphaTensor (DeepMind, Nature 2022) - Reinforcement learning agent that discovers provably correct and faster matrix multiplication algorithms via tensor decomposition games, finding a 47-multiplication algorithm for 4x4 matrices (improving on Strassen's 49 from 1969) and releasing 14,236+ nonequivalent discovered factorizations with GPU benchmarking scripts (Google DeepMind, 2.9K+ stars, Apache 2.0)
- AlphaDev (DeepMind, Nature 2023) - Deep reinforcement learning agent that discovers fundamental algorithms from scratch by transforming algorithm discovery into a single-player game, discovering new provably correct sorting routines up to 70% faster for short sequences and an improved hashing algorithm; the AlphaDev sorting routines were incorporated into the LLVM libc++ C++ standard library used by millions of developers (Google DeepMind, official code, 740+ stars, Apache 2.0)
- OpenEvolve - Open-source implementation of AlphaEvolve's evolutionary coding agent paradigm, enabling LLMs to autonomously discover and optimize algorithms through iterative evolution, matching the approach behind DeepMind's breakthrough matrix multiplication discovery (6.2K+ stars, 2025)
- Darwin Gödel Machine (Sakana AI) - Open-ended self-improving agent that iteratively rewrites its own codebase and empirically validates each mutation on coding benchmarks (SWE-bench, Polyglot), demonstrating open-ended evolution where agents improve their ability to improve themselves, diverging into a population of diverse specialists (arXiv 2505.22954, 2.3K+ stars, Apache 2.0, 2025)
- SkyDiscover - Modular framework for AI-driven scientific and algorithmic discovery, providing a unified interface for implementing, running, and fairly comparing discovery algorithms across 200+ optimization tasks; introduces AdaEvolve and EvoX adaptive/evolutionary algorithms and natively supports OpenEvolve, GEPA, and Harbor-format benchmarks (skydiscover-ai, 568+ stars, Apache 2.0, 2026)
Evaluation & Benchmarking
- ScienceAgentBench (ICLR 2025) - 102 executable tasks from 44 peer-reviewed papers across 4 disciplines with containerized evaluation
- AIRS-Bench (Meta, 2026) - Benchmark quantifying end-to-end autonomous AI research abilities of LLM agents across 20 tasks from SOTA machine learning papers spanning NLP, code, math, biochemical modelling, and time series forecasting, with normalized score metrics against human SOTA and HuggingFace dataset
- PaperBench (OpenAI, 2025) - Benchmark evaluating AI agents' ability to replicate 20 ICML 2024 Spotlight/Oral papers from scratch, with 8,316 gradable tasks and author-co-developed rubrics
- PaperGuru (AutoTrustAI, 2026) - Lifecycle-Aware Memory (LAM) primitive and benchmark for long-horizon research agents, achieving 65.95% mean reproduction on PaperBench and 94.66% on SurveyBench through Capital Chunk Memory (CCM) with versioned content, structural multi-hop relevance, and provenance-grounded composition; 10 peer-reviewed acceptances at FSE/ICML/TOSEM/AEI/ICoGB (1.3K+ stars)
- MLE-Bench (OpenAI, 2024) - Benchmark evaluating AI agents on 75 curated Kaggle-style ML engineering competitions with reproducible Docker-based grading harness, human baselines, and end-to-end task lifecycle, used as a primary benchmark for autonomous ML research agents (e.g., InternAgent #1 at 36.44%)
- ScienceBoard (ICLR 2026) - Evaluating multimodal autonomous agents in realistic scientific workflows across real scientific software environments (KAlgebra, Celestia, Grass GIS, Lean 4, etc.) with VM-based evaluation infrastructure and agent trajectories
- BuildArena - First physics-aligned interactive benchmark for LLM agents in engineering construction, designing rockets/cars/bridges in physics simulator with 3D spatial geometry library
- SciTrust (2024) - Trustworthiness evaluation framework for scientific LLMs (truthfulness, hallucination, sycophancy)
Academic Review & Evaluation
- AgentReview - LLM agents simulating academic peer review ecosystems
- LLM-Peer-Review - Web application for LLM-assisted manuscript review and annotation
Domain-Specific Research Agents
- Aletheia - 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) - 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) - 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 - 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 - 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 - 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)
- 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)
🏷 Data Labeling & Curation
Weak Supervision & Auto-Labeling
- Snorkel - Programmatic data labeling and weak supervision for scientific datasets
- PandasAI - Conversational data analysis and visualization using natural language
- Cleanlab - Standard data-centric AI package for data quality and machine learning, automatically detecting label errors, outliers, and dataset issues to improve scientific dataset reliability and model performance (11K+ stars, MIT License)
⚗ Scientific Machine Learning
Neural Differential Equations
Chemical Reaction Networks & Systems Biology
- Catalyst.jl - Chemical reaction network and systems biology interface for scientific machine learning (SciML), enabling high-performance, GPU-parallelized simulation and analysis of complex biochemical systems with O(1) solvers (SciML, 518+ stars, Julia)
Physics-Informed Neural Networks
- DeepXDE - Deep learning library for solving PDEs
- Lang-PINN - LLM-driven multi-agent system that builds trainable PINNs from natural language task descriptions, achieving 3-5 orders of magnitude MSE reduction and 50%+ execution success improvement (ICLR 2026)
- PINNs - Physics-informed neural networks
- NVIDIA PhysicsNeMo - Open-source framework for building physics-ML models at scale (renamed from Modulus, 2025)
- PINA - Physics-Informed Neural networks for Advanced modeling in PyTorch
- NeuroMANCER (PNNL) - PyTorch-based differentiable programming framework for physics-informed system identification, parametric constrained optimization, and model predictive control, integrating neural operators, neural ODEs, KANs, SINDy, and differentiable predictive control with 30+ tutorials (1.3k+ stars, BSD License)
- SciANN - Keras-based scientific neural networks
- NeuralPDE.jl - Physics-informed neural networks in Julia
Neural Operators & Model Discovery
- DeepONet - Learning nonlinear operators
- PySINDy - Sparse identification of nonlinear dynamics
- PyDMD (JOSS 2021) - Python package for Dynamic Mode Decomposition (DMD) and its variants (exact, incremental, multiresolution MR-DMD, higher-order HODMD, parametric, BOP-DMD, DMD-control, and DL-DMD neural-network extensions), enabling data-driven reduced-order modeling, Koopman operator analysis, and flow-field feature extraction for high-dimensional dynamical systems in fluid dynamics and scientific computing (1.2K+ stars, MIT License, actively maintained)
- PySR - High-performance symbolic regression for discovering interpretable scientific equations from data, multi-population evolutionary search with Python/Julia backend, widely used in physics and astronomy (Cambridge, NeurIPS 2023)
- LLM-SR - Scientific equation discovery and symbolic regression using LLMs, combining code generation with evolutionary search (ICLR 2025 Oral)
- PSRN - Parallel symbolic regression network evaluating millions of expressions on GPU with automated subtree reuse, Nature Computational Science cover article (MIT, 2026)
- pykan - Kolmogorov-Arnold Networks with learnable activation functions on edges instead of fixed node activations, achieving strong performance in function fitting, PDE solving, and scientific discovery with enhanced interpretability as an alternative to MLPs (MIT, 16.3K+ stars, 2024)
- JEPA-Anything (Gen-Verse, arXiv 2026) - Domain-agnostic predictive world-model framework built on joint-embedding predictive architectures (JEPAs), organizing latent world states into complementary factors via Orthogonal Predictive Factorization (OPF) with dedicated prediction pathways recombined for downstream readout, intervention prediction, planning, and multi-step rollout; shares one predictive core and latent state interface across cells, molecules, physical fields, control environments, and clinical trajectories, with a reusable core library, task-design tools, and HuggingFace model collection (377+ stars, Apache 2.0, 2026)
Simulation-Based Inference
- sbi - Python package for simulation-based inference enabling likelihood-free Bayesian parameter estimation from scientific simulators, with flexible interfaces for neural posterior estimation, sequential methods, and MCMC/variational backends (Mackelab, 825+ stars)
📖 Papers & Reviews
Foundational Papers
📊 Comprehensive Surveys & Reviews (2024-2025)
AI for Scientific Research
Scientific Large Language Models
Scientific Machine Learning
Uncertainty Quantification
Automation & Self-Driving Laboratories
Policy & Strategic Perspectives
- Artificial Intelligence for Science (CSIRO 2022) - Landmark report analyzing AI adoption across 98% of scientific fields over 60 years
- AI for Science 2025 (Fudan University & Nature 2025) - Comprehensive report on AI's transformative impact across 7 scientific fields, 28 research directions, and 90+ challenges
- AI in science evidence review (European Scientific Advice 2024) - Policy-focused evidence review on AI's impact in research
🚀 AI Scientist & Autonomous Research (2024-2025 Breakthroughs)
Recent Advances & Domain Applications
📈 Evaluation & Benchmarking
🔬 Domain-Specific Applications
🧬 Biology & Medicine
Protein & Drug Discovery
- CryoDRGN - Neural network-based cryo-EM heterogeneous reconstruction, modeling continuous 3D structure distributions from single-particle images, with CryoDRGN-ET extending to in-cell cryo-electron tomography (MIT CSAIL, Nature Methods 2021/2024)
- ModelAngelo - Automatic atomic model building program for cryo-EM maps using deep learning, enabling rapid de novo protein structure determination from electron density with high accuracy (3DEM/EMBL, 169+ stars)
- Topaz (Nature Communications 2019) - Deep-learning particle-picking framework for cryo-electron microscopy using positive-unlabeled convolutional neural networks that detect protein particles in micrographs at competitive precision with far fewer labeled examples than fully supervised baselines, serving as the automated front end of cryo-EM structure determination pipelines and adopted alongside CryoSPARC/RELION workflows with pre-trained general models, micrograph denoising, and CPU/GPU inference (Bepler Lab, MIT, 222+ stars, GPL-3.0, actively maintained 2019-2026)
- AlphaFold - Protein structure prediction
- AlphaFold3 - AlphaFold 3 inference pipeline for unified biomolecular structure prediction of proteins, nucleic acids, small molecules, ions, and post-translational modifications (Google DeepMind, Nature 2024)
- AlphaFold Server - Free, easy-to-use web platform by Google DeepMind and Isomorphic Labs for running AlphaFold 3 predictions of biomolecular structures and interactions, enabling researchers without local infrastructure to model proteins, nucleic acids, small molecules, ions, and post-translational modifications through a searchable proteome interface (2024)
- AlphaProteo - Deep learning system for de novo design of high-affinity protein binders, achieving strong binding across diverse target classes including challenging intracellular proteins with significantly higher success rates than traditional wet-lab screening methods (Google DeepMind, Nature 2024)
- AlphaPulldown - Automated pipeline for proteome-scale protein-protein interaction screening with AlphaFold-Multimer and AlphaFold 3, supporting flexible inputs (UniProt IDs, FASTA, residue regions, multimers, AF3 JSON features) and integrated downstream analysis for hit prioritization (Kosinski Lab, EMBL, Nature Protocols 2024, 317+ stars, GPL-3.0)
Genomics & Bioinformatics
- RhoFold+ - End-to-end RNA 3D structure prediction using RNA language model pretrained on 23.7M sequences, outperforming existing methods and human expert groups on RNA-Puzzles and CASP15 (Nature Methods 2024)
- NuFold (Nature Communications 2025) - End-to-end deep learning approach for RNA tertiary structure prediction with a flexible nucleobase center representation, achieving ~7 Å C1' RMSD across test RNAs and predicting ~545,000 structures covering 2,200+ RNA families (Kihara Lab, Purdue University, 50+ stars)
- RNA-FM (Nature Methods 2024) - RNA foundation model trained on millions of RNA sequences for generalist RNA sequence understanding, enabling downstream structure prediction, function annotation, and representation learning for non-coding RNAs (ml4bio, 372+ stars)
- RiNALMo (Nature Communications 2025) - General-purpose RNA language model with 650M parameters pretrained on 36M non-coding RNA sequences, achieving strong generalization on structure prediction tasks including secondary structure prediction, splice-site prediction, mean ribosome loading, and ncRNA classification (lbcb-sci, 165+ stars, Apache-2.0)
- RNAPro (NVIDIA, 2026) - State-of-the-art RNA 3D folding model developed with Stanford Das Lab and Kaggle competition winners, featuring a 488M-parameter AF3-like architecture with MSA and template-based modeling, enabling structure-driven drug discovery and RNA therapeutics design (NVIDIA-Digital-Bio, Apache 2.0)
- gRNAde - Generative AI framework for inverse design of 3D RNA structure and function using geometric deep learning, learning design rules from 3D structures to capture complex tertiary interactions (pseudoknots, non-canonical base pairs) with expert-level accuracy for designing functional RNAs including aptamers and ribozymes (bioRxiv 2025)
- AIDO.ModelGenerator - GenBio AI's software stack for the AI-Driven Digital Organism, supporting adaptation and finetuning of multiscale biological foundation models across DNA, RNA, protein, structure, and single-cell tasks with reproducible CLIs and pretrained model zoo (2025)
Neuroscience & Behavioral Analysis
- DeepLabCut - Markerless pose estimation of user-defined features with deep learning for all animals including humans, enabling quantitative behavioral analysis in neuroscience and ethology (Nature Neuroscience 2018, 5.6K+ stars)
- SLEAP - Deep learning-based multi-animal pose tracking and behavior classification, enabling automated quantification of social interactions and collective behavior across species (Nature Methods 2022, 2.2K+ stars)
- NeuroAI (Meta FAIR) - Modular Python suite for Neuro-AI research across all modalities, providing efficient data loaders (NeuralSet), curated datasets (NeuralFetch), scalable training (NeuralTrain), and unified benchmarking (NeuralBench) for building and evaluating neuroscience foundation models (Meta FAIR, 270+ stars, MIT License, 2026)
- CEBRA (Nature 2023) - Learnable latent embeddings for joint behavioral and neural analysis, enabling consistent and interpretable mapping of neural activity to behavior across modalities, species, and experiments (EPFL & Harvard, 1K+ stars)
- Kilosort (Nature Methods 2024) - Fast spike sorting with drift correction for extracellular electrophysiology, enabling universal neural spike sorting via deep learning on high-density neural probe recordings (MouseLand, 609+ stars)
- 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) - 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 - 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)
Computational Pathology & Digital Pathology
- UNI (Nature Medicine 2024) - General-purpose pathology foundation model pretrained on 100K+ diagnostic whole-slide images across 20 major tissue types, achieving state-of-the-art transfer learning across 30+ clinical tasks and serving as a universal feature extractor for digital pathology (Mahmood Lab, 722+ stars)
- Prov-GigaPath (Nature 2024) - Whole-slide pathology foundation model trained on 1.3 billion image tiles from 171K slides using a LongNet-based architecture to encode gigapixel-scale WSIs for cancer subtyping and biomarker prediction (Microsoft Research & Providence, 601+ stars)
- GigaTIME (Cell 2025) - Multimodal AI system generating virtual populations for tumor microenvironment modeling from H&E and multiplex immunofluorescence pathology images, enabling large-scale spatial analysis of cancer biology and therapeutic response prediction (Microsoft Research & Providence, 370+ stars)
- CONCH (Nature Medicine 2024) - Vision-language pathology foundation model using contrastive learning on histopathology image-text pairs, enabling zero-shot classification, slide-level retrieval, and multimodal reasoning across diverse cancer types (Mahmood Lab, 494+ stars)
- PLIP (Nature Medicine 2023) - First vision-and-language foundation model for pathology AI, fine-tuned from CLIP on 249K image-caption pairs, enabling open-ended visual-semantic search and zero-shot diagnosis across histopathology (Pathology Foundation, 376+ stars)
- TITAN (Nature Medicine 2024) - Multimodal whole-slide pathology foundation model jointly pretrained on H&E histology and diagnostic text reports, enabling zero-shot cancer subtyping, biomarker prediction, and multimodal reasoning across diverse cancer types (Mahmood Lab, 341+ stars)
- Virchow (Nature Medicine 2024) - Self-supervised pathology foundation model (ViT-Huge, 632M parameters) pretrained via DINOv2 on 1.5M whole-slide images from Memorial Sloan Kettering across 17 cancer types, with Virchow2 follow-up scaling to 3.1M slides and mixed magnifications, achieving SOTA on biomarker prediction, mutation classification, and rare cancer detection (Paige AI & MSK)
Medical AI & Clinical Applications
- Cellpose - Generalist deep learning algorithm for cell and nucleus segmentation across diverse image types, with human-in-the-loop training (2.0) and one-click image restoration (3.0), 70K+ training objects (Nature Methods 2021/2022/2025)
- StarDist - Deep learning-based object detection and segmentation for star-convex shapes, widely adopted for cell and nucleus segmentation in fluorescence and electron microscopy via a compact neural network architecture with non-maximum suppression and shape-based post-processing (Nature Methods 2020, 1.2K+ stars)
- InstanSeg (Nature Methods 2025) - PyTorch-based embedding instance segmentation algorithm optimized for accurate, efficient, and portable cell and nucleus segmentation across fluorescence and brightfield microscopy images, achieving state-of-the-art speed and accuracy with lightweight model sizes suitable for edge deployment (224+ stars, Apache 2.0)
- napari - Fast, interactive, multi-dimensional image viewer for Python, foundational platform for scientific imaging AI with a rich plugin ecosystem integrating deep learning segmentation, object tracking, and microscopy analysis workflows (2.6K+ stars)
- cellSAM - Foundation model for universal cell segmentation achieving state-of-the-art performance across bacteria, tissue, yeast, cell culture, and diverse imaging modalities (brightfield, fluorescence, phase), with pip-installable inference and Napari plugin (vanvalenlab/Caltech, bioRxiv 2024)
- micro-sam - Segment Anything Model for microscopy: interactive and automatic segmentation of light, electron, and fluorescence microscopy images in 2D and 3D, with domain-specific fine-tuning workflows for scientific imaging (1.5K+ stars)
- MedSAM - Universal medical image segmentation foundation model trained on 1.57M image-mask pairs across 10 imaging modalities and 30+ cancer types (Nature Communications 2024)
- MedSAM2 - Segment Anything in 3D medical images and videos, extending SAM2 to volumetric and temporal medical imaging with state-of-the-art zero-shot segmentation performance across CT, MRI, and surgical video (arXiv 2025)
⚛ Chemistry & Materials
LLM for Chemistry
- LLM4Chemistry - Curated paper list about LLMs for chemistry covering fine-tuning, reasoning, multi-modal models, agents, and benchmarks (COLING 2025)
- ChemMCP - Extensible chemistry toolkit for MCP-enabled AI assistants, exposing molecule analysis, property prediction, and reaction synthesis tools through unified Python/MCP interfaces for chemistry agents and research workflows (Apache 2.0, 2025)
- MoleCode - LLM-native molecular language that represents molecules as explicit graph-based code, enabling LLMs to operate and reason on chemistry directly with 5× lower token cost and ~76-80% accuracy on novel molecules vs ~20% for SMILES; supports small molecules, polymers, and Markush structures with lossless RDKit interconversion and Claude Code/Codex agent skills (AtomFlow, arXiv:2605.16480, 281+ stars, MIT License, 2026)
Materials Discovery
- GNoME - DeepMind's graph neural network for materials exploration, discovering 2.2M new crystal structures (380K most stable) equivalent to 800 years of traditional research, with 520K+ materials dataset open-sourced (Nature 2023)
- FAIRChem (OMat24) - Meta's comprehensive ML ecosystem for materials/chemistry with 118M+ DFT calculations, EquiformerV2 models achieving top Matbench Discovery performance
- UMA (Meta FAIR, 2025-2026) - Universal Model for Atoms: single pretrained universal interatomic potential jointly trained on 235M+ DFT calculations spanning inorganic materials (OMat24, sAlex) and molecules (OMol25), delivering quantum-accurate energies and forces across materials, molecules, catalysts, polymers, and biomolecules; UMA-1.2 is ~50% faster and ~40% more accurate on molecules, with open weights, pip-installable
fairchem-core, and LAMMPS/multi-GPU inference (Meta FAIR, 2026)
- Skala 1.1 (Microsoft Research, 2026) - Neural network-based exchange-correlation functional for density functional theory (DFT) that surpasses state-of-the-art hybrid functionals in accuracy for main-group thermochemistry, kinetics, and non-covalent interactions at semi-local DFT cost; includes PySCF/GPU4PySCF/ASE bindings and C++/Fortran integrations (248+ stars, MIT License)
- All-atom Diffusion Transformers (ADiT) - Unified latent diffusion transformer that jointly generates periodic crystals and non-periodic molecules, scaling to 500M parameters with SOTA results on QM9, MP20, and GEOM-DRUGS (Meta FAIR, ICML 2025, 310+ stars)
- JARVIS - NIST's open-source platform for data-driven atomistic materials design, integrating DFT datasets (JARVIS-DFT), machine learning property prediction (JARVIS-ML), and a comprehensive leaderboard for benchmarking materials AI methods across the periodic table (384+ stars)
- NVIDIA ALCHEMI Toolkit - Developer toolkit for accelerating training and inference for AI in chemistry and material science, providing optimized GPU-accelerated workflows for molecular and materials machine learning (NVIDIA, 2026)
Chemical Synthesis
- AiZynthFinder - AstraZeneca's industrial-grade retrosynthetic planning tool using MCTS to recursively decompose molecules into purchasable precursors, with multi-step route scoring and support for custom one-step models (v4.0, 2024)
- Molecular Transformers - AI for chemical reaction prediction and synthesis planning
- SyntheMol (Stanford, Nature Machine Intelligence 2024) - Generative AI system for antibiotic discovery that searches billions of synthesizable molecules by combining molecular building blocks through real chemical reactions, experimentally validating novel compounds active against drug-resistant bacteria
Lab Automation & Robotics
- 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 - 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)
- BayBE (EMD Group / Merck KGaA) - Open-source Bayesian optimization and design-of-experiments framework serving as the optimization back end of self-driving laboratory campaigns, including the AlphaFlow autonomous synthesis platform (Nature 2024); provides surrogate models, active/transfer learning strategies, chemistry-aware encodings (RDKit fingerprints, descriptors), and botorch-based uncertainty handling with a unified, pip-installable API (513+ stars, Apache 2.0, 2023-2026)
🌌 Physics & Astronomy
Machine Learning for Physics
- AlphaQubit - Google DeepMind and Google Quantum AI's transformer-based neural-network decoder for quantum error correction, trained on real Sycamore quantum processor data to outperform tensor-network and correlated matching decoders at code distances 3 and 5, demonstrating ML's role in enabling fault-tolerant quantum computing (Nature 2024)
- FermiNet - DeepMind's neural network for ab-initio quantum chemistry, directly solving the many-electron Schrödinger equation via variational Monte Carlo with antisymmetric wavefunctions, extended to excited states (Phys. Rev. Research 2020, Science 2024)
- NetKet - Machine learning toolkit for many-body quantum systems, implementing neural quantum states, variational Monte Carlo, and tensor network algorithms to solve ground-state and dynamical problems in condensed matter physics and quantum chemistry (EPFL & collaborators, Nature Physics 2019/2022+, 670+ stars)
- DeepQMC - Open-source JAX-based software suite for variational optimization of deep-learning molecular wave functions, solving electronic ground and excited states via neural-network trial wave functions with configurable FermiNet, PauliNet, Psiformer, LapNet, and DeepErwin ansätze, geometric transferability across molecular configurations, and effective core potential support (FU Berlin / Noé group, J. Chem. Phys. 2023, 420+ stars, MIT License)
- ParT / Particle Transformer (Nature Machine Intelligence 2024) - Transformer architecture for high-energy physics that treats particle jets as unordered sets of constituent particles, using pairwise particle-particle attention to capture interparticle interactions; achieves state-of-the-art jet tagging on the large-scale JetClass benchmark and is widely adopted in LHC flavor-tagging pipelines (Meta FAIR & Penn State, 135+ stars, MIT License, 2023-2024)
- JAX-MD - Molecular dynamics in JAX
- Neural ODEs - Differential equations with neural networks
- Physics-Informed Neural Networks - Physics-constrained ML
Astronomy & Astrophysics
- 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)
- AION (arXiv 2025) - Polymathic AI's large omnimodal foundation model for astronomical surveys, seamlessly integrating 39 distinct data modalities including imaging, spectra, photometry, and catalog entries for similarity search, property prediction, and generative modeling across legacy surveys (MIT)
- ML4GW (A3D3 Institute, JOSS 2025) - PyTorch library for training neural networks on gravitational-wave physics, providing differentiable PSD estimation, whitening, SNR calculation, interferometer response projection, waveform simulation, and streaming data loaders; the shared back end of the NSF A3D3/ML4GW pipelines deployed for real-time detection of compact-binary coalescences in LIGO–Virgo–KAGRA observing runs (35+ stars, GPL-3.0, 2022-2026)
- AstroPy - Python astronomy tools
- Gaia Archive - Stellar data for ML
- DeepSphere - Spherical CNNs for astronomy
🌍 Earth & Climate Science
Climate Modeling
- GenCast - Google DeepMind's diffusion-based ensemble weather forecasting model at 0.25° resolution, outperforming ECMWF ENS on 97.2% of targets up to 15 days ahead, with open-source code and weights (Nature 2024)
- WeatherNext (Google DeepMind, 2025-2026) - Family of operational-quality open weather models from DeepMind and Google Research, including WeatherNext Graph (deterministic GNN medium-range forecasting, published as GraphCast), WeatherNext Gen (diffusion ensemble, published as GenCast), WeatherNext 2 (state-of-the-art global medium-range and cyclone forecasting skillful beyond 15 days, operational at 0.25° resolution), and WeatherNext Cyclones (breakthrough tropical cyclone track forecasting, Nature 2026); official code and open weights (Apache 2.0, 7.6K+ stars)
- Aurora - Microsoft's foundation model for the Earth system supporting weather, air pollution, and ocean wave forecasting at multiple resolutions, trained on 1M+ hours of diverse atmospheric data (Nature 2025)
- Aardvark Weather (Cambridge MLIP, Nature 2025) - First end-to-end data-driven weather prediction system learning directly from raw, heterogeneous Earth observations rather than physics-based reanalysis, producing both global gridded and arbitrary station forecasts; releases model weights, training code, and an ML-ready observational dataset (2007-2019) for building future end-to-end weather models (221+ stars, CC0-1.0)
- Earth-Copilot - Microsoft's AI-powered geospatial Earth science application for natural-language exploration, visualization, and analysis of 130+ satellite collections, with STAC integration, multi-agent backend, MCP server, and deployable React/FastAPI stack (MIT, 2025)
- U-Cast (Rose-STL-Lab, ICML 2026) - Surprisingly simple and efficient frontier probabilistic weather forecaster built on a standard U-Net trained with deterministic MAE pre-training followed by short CRPS fine-tuning via Monte Carlo Dropout, matching or exceeding the probabilistic skill of GenCast and IFS ENS at 1.5° resolution with >10× less training compute than leading CRPS models and >10× lower inference latency than diffusion models; trains in under 12 H200 GPU-days and generates a 15-day ensemble forecast in 3 seconds, with official code and pretrained checkpoints (UCLA, 41+ stars, Apache 2.0)
Hydrology & Water Resources
- NeuralHydrology (KIT, HESS 2019/2021) - Python library from KIT for training deep learning models on large-sample hydrology, introducing LSTM-based rainfall-runoff modeling that learns universal, regional, and local hydrological behaviors from hundreds of catchments; widely used for streamflow forecasting, flood prediction, and drought research, supporting the CAMELS and Caravan benchmark datasets (581+ stars, BSD-3-Clause, actively maintained)
Geophysics & Seismology
- SeisBench - A toolbox for machine learning in seismology, providing unified interfaces for deep learning seismic phase picking, earthquake detection, and waveform analysis across multiple benchmark datasets and pretrained models (397+ stars, actively maintained)
- EQTransformer (Nature Communications 2020) - Self-attention transformer performing simultaneous earthquake detection and P/S phase picking on continuous seismic waveforms, trained on the large-scale STEAD benchmark dataset and outperforming legacy detection methods with far fewer false positives; widely adopted by seismological observatories for earthquake monitoring, included in SeisBench model collections, and extended by efficient EQT-Mini/EQT-Lite successors for real-time deployment (Stanford, 418+ stars, MIT License, actively maintained)
- PhaseNet (GJI 2019) - U-Net-style deep neural network for P/S seismic arrival-time picking trained on millions of waveforms from the Northern California Earthquake Data Center, achieving near-analyst picking precision at orders-of-magnitude higher speed and robustness to low signal-to-noise traces where STA/LTA fails; a foundational reference for deep-learning phase picking, integrated into SeisBench model collections and national seismic networks, with PhaseNet-DAS extending it to distributed acoustic sensing (Stanford AI4EPS, 386+ stars, MIT License, actively maintained)
Remote Sensing & Geospatial AI
- TorchGeo - PyTorch domain library for geospatial deep learning providing standardized datasets, samplers, transforms, and pre-trained models for remote sensing, land cover mapping, and environmental monitoring (Microsoft, 4K+ stars)
- Prithvi-EO-2.0 (IBM & NASA, 2024) - Versatile multi-temporal geospatial foundation model for Earth observation, built on a ViT-based masked autoencoder with 3D spatiotemporal patch embeddings and geolocation/temporal metadata encoding; pretrained on 4.2M global time-series samples from NASA's Harmonized Landsat and Sentinel-2 archive at 30m resolution, with 300M/600M parameter variants and fine-tuning configs for flood detection, wildfire scar, landslide detection, crop segmentation, land cover, and biomass estimation (258+ stars, MIT License)
- Clay Foundation Model - Open-source self-supervised vision foundation model for Earth observation by Clay Foundation (non-profit), a Masked Autoencoder ViT pretrained on multimodal satellite imagery (Sentinel-1/2, Landsat 8-9, NAIP, MODIS, LINZ DEM) with location/time embeddings, supporting classification, segmentation, change detection, similarity search, and few-shot downstream geospatial tasks (Apache 2.0, v1.5 2024-2025)
- Satlas - Allen Institute for AI's global geospatial foundation model for satellite imagery analysis, enabling large-scale mapping of buildings, wind turbines, trees, and land cover from Sentinel-2 data with open-source weights and inference tools (2024)
- 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) - 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) - 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)
🌾 Agriculture & Ecology
Agricultural AI
- PlantNet - Plant identification using AI and citizen science
- AgML - Agricultural machine learning platform
- FarmVibes.AI - Multi-modal geospatial ML platform for agriculture and sustainability, fusing satellite imagery (RGB, SAR, multispectral), drone imagery, weather data, and sensor data for crop identification, carbon footprint estimation, and microclimate prediction (Microsoft Research, MIT License)
- PlantCV - Open-source image analysis toolkit for high-throughput plant phenotyping, extracting morphological, color, and texture traits from RGB, hyperspectral, and thermal imagery with modular Python workflows for crop improvement, stress detection, and plant biology research (Donald Danforth Plant Science Center, 795+ stars, MPL-2.0)
- Virdis - Satellite-powered agricultural and land analytics platform combining Sentinel-2 imagery, Google Earth Engine processing, real-time weather data, soil science databases, and AI-driven crop planning into a unified web dashboard (145+ stars, AGPL-3.0, 2026)
- Agribound - AI-powered field boundary delineation toolkit combining satellite foundation models, embeddings, and global training data for accurate agricultural parcel/field boundary mapping, with Google Earth Engine integration and PyPI distribution (84+ stars, Apache 2.0, 2026)
Ecological Modeling
- BioSimulators - Biological simulation tools
- EcoNet - Ecological modeling and conservation AI
- BioCLIP (CVPR 2024) - Vision foundation model for the tree of life, pretrained on diverse biological imagery across taxa for zero-shot species identification, trait extraction, and biodiversity research (Ohio State University Imageomics Institute)
- BioCLIP 2 (NeurIPS 2025 Spotlight) - Biological vision foundation model trained on TreeOfLife-200M, yielding extraordinary accuracy on diverse biological visual tasks including habitat classification and trait prediction despite a narrow training objective (Ohio State University Imageomics Institute)
- Microsoft Biodiversity - Microsoft AI for Good Lab's open-source biodiversity research hub providing AI models, edge devices, and tools for wildlife monitoring and conservation, including MegaDetector (camera trap animal detection), SPARROW (species recognition), PytorchWildlife (conservation AI toolkit), and bioacoustics analysis pipelines (1K+ stars)
- BirdNET-Analyzer - Deep learning-based bioacoustic monitoring framework for automated bird species identification from audio recordings, supporting 6,000+ species globally with real-time analysis, batch processing, and API deployment; foundational tool in biodiversity research, conservation biology, and ecological acoustic monitoring (Cornell Lab of Ornithology, 1.5K+ stars, MIT License)
🧠 Social Sciences
Social Science Research & Simulation
- AgentSociety - Modern LLM-native agent simulation platform for social science research and experimental design, providing a flexible framework for creating and managing intelligent agents in simulated environments (Tsinghua FIB Lab, 984+ stars, 2025)
- Auto-Empirical-Research-Skills - Curated collection of 23,000+ agent skills for empirical research across 8 social science disciplines, enabling reproducible social science research with AI agents (Stanford REAP & CoPaper.AI, 3K+ stars, 2026)
- EDSL - Design, conduct and analyze results of AI-powered surveys and experiments. Simulate social science and market research with large numbers of AI agents and LLMs (460+ stars, 2024)
- GABRIEL (OpenAI, 2026) - Generalized Attribute Based Ratings Information Extraction Library; official OpenAI toolkit that turns messy qualitative corpora into analysis-ready datasets for social scientists and data scientists, measuring quantitative attributes in text, images, or audio using the GPT API. See the official blog post and NBER working paper (413+ stars, Apache 2.0)
🏗 Engineering & Built Environment
Mechanical, Aerospace & Industrial Engineering
- Noether (Emmi AI) - Open software framework for Engineering AI built on transformer building blocks, enabling teams to build, train, and operate industrial simulation models across engineering verticals; includes ready-to-use recipes for CFD (AB-UPT on DrivAerML), external aerodynamics, and heat transfer (234+ stars, ENPL non-commercial license, 2026)
Structural & Civil Engineering
- StructureClaw - AI-assisted structural engineering workspace for AEC workflows: natural language to structural model, analysis, code-check, and report (171+ stars, MIT License, 2026)
Construction & Built Environment Management
- OpenConstructionERP - Open-source construction ERP with AI-powered cost matching, BOQ generation, and PDF/CAD/BIM takeoff; 42 regional catalogues, 21 languages, 71 modules (DataDrivenConstruction, 717+ stars, AGPL-3.0, 2026)
Architectural Design & BIM
- 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 - 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)
- Circuit Training / AlphaChip (Google Research, Nature 2021) - Open-source framework behind AlphaChip, the first ML system to design superhuman chip floorplans used in production Google TPU blocks; trains edge-based graph neural networks with distributed PPO and fine-tunes with simulated annealing to place macros under timing, congestion, and density constraints, generalizing across chip netlists (Google Research, 1.7K+ stars, Apache 2.0)
🤖 Foundation Models for Science
General Science Models
- Galactica - Large language model for science
- Intern-S1 - Open-source scientific multimodal foundation model built on a 235B MoE LLM and 6B vision encoder, continually pretrained on 5T tokens including 2.5T scientific-domain tokens, with strong results across chemistry, materials, life science, and earth science benchmarks (2025)
- Llemma - Open language model for mathematics (7B/34B) trained on Proof-Pile-2, outperforming Minerva at equal scale on MATH benchmark, with tool use and formal theorem proving in Lean without finetuning (EleutherAI, ICLR 2024)
- TimesFM (Google Research) - Pretrained time series foundation model for long-horizon forecasting across diverse scientific domains including climate variables, biomedical signals, and physical observations; decoder-only Transformer architecture with strong zero-shot generalization (19.8K+ stars, Apache 2.0, 2024-2025)
- Chronos (Amazon Science, NeurIPS 2024) - Pretrained time series foundation model for zero-shot forecasting across diverse scientific and real-world domains; tokenizes continuous time series into discrete bins to train transformer language models on large-scale corpora, achieving strong zero-shot generalization and competitive performance with task-specific supervised models on climate, energy, and health benchmarks (5.3K+ stars, Apache 2.0, 2024-2026)
- Moirai-MoE (Salesforce AI Research, NeurIPS 2024) - Universal time series forecasting via the UNI2TS library, training a single transformer with shared self-attention and specialized mixture-of-experts feed-forward blocks to achieve strong zero-shot generalization across heterogeneous domains including energy, weather, transportation, and health time series (1.6K+ stars, Apache 2.0, 2024-2026)
- Moment (CMU Auton Lab, ICML 2024) - Family of open time-series foundation models (MOMENT-1 small/base/large, T5-style encoder-only architecture with time series patching) pretrained on the curated Time Series Pile corpus, enabling zero/few-shot forecasting, classification, anomaly detection, imputation, and representation learning across scientific and real-world temporal data, with open weights on HuggingFace and pip-installable package (Auton Lab, CMU & UPenn, MIT License, 850+ stars)
Domain-Specific Models
- ESM - Protein language models
- BioNeMo Framework - NVIDIA's open-source platform for building and adapting biological AI models at scale, bundling ESM-2, Geneformer, MolMIM and DNA embedding models with recipes for single-GPU to multi-node training (2025)
- BioNeMo Recipes (NVIDIA) - TransformerEngine-accelerated checkpoints and training recipes for scaling biological foundation models (ESM-2, AMPLIFY, Geneformer, CodonFM) from single-GPU prototyping to multi-node FSDP training with FP8/MXFP8/NVFP4 precision, compatible with PyTorch, HF Accelerate, and PyTorch Lightning, plus sparse-autoencoder interpretability tools for biological foundation models (851+ stars, 2026)
- IBM FM4M - IBM's open foundation model family for materials and chemistry, covering SMILES, SELFIES, molecular graphs, 3D atom positions, and electron density grids, with a unified toolkit for representation learning and downstream prediction/generation (Apache 2.0, 2024-2025)
- ChemGPT - Chemistry-focused language model
- BioGPT - Biomedical text generation
- HuatuoGPT-o1 (2025) - Open-source medical large language model for complex clinical reasoning, extending the o1 long-chain-of-thought paradigm to biomedical question answering and diagnostic inference (FreedomIntelligence, 1.3K+ stars)
- Med-Gemma (Google Health, 2025) - Google's open multimodal medical LLM family built on Gemma 3, including a 4B multimodal model handling medical images (radiology, pathology, dermatology) alongside text and a 27B text model for clinical reasoning; trained on de-identified medical data with checkpoints and inference code released under Apache 2.0 (1.6K+ stars)
- AntAngelMed (2026) - 103B-parameter open-source medical language model with 1/32 Mixture-of-Experts architecture, achieving HealthBench-leading performance among open-source models with only 6.1B active parameters; jointly developed by Ant Group and Zhejiang Province Health Information Center (MIT License)
📈 Datasets & Benchmarks
Multidisciplinary
Biology & Medicine
- TDC - Therapeutics Data Commons: 66 AI-ready datasets across 22 drug discovery tasks with 29 leaderboards, covering target identification, molecular generation, ADMET prediction, and clinical trial outcomes (Harvard MIMS, NeurIPS 2021/2024)
- ProteinGym - Large-scale benchmark suite for protein fitness prediction and design, aggregating 200+ deep mutational scanning assays and clinical variant datasets across diverse protein families and taxa, with standardized zero-shot and supervised leaderboards for variant effect prediction, mutation effect prediction, and protein language model evaluation (OATML & Marks Lab, NeurIPS 2023 Spotlight, Datasets & Benchmarks)
- ProteinWorkshop - Unified benchmarking framework for protein representation learning, providing standardized interfaces for pre-training and diverse downstream tasks including structure prediction, fitness prediction, and property prediction across multiple protein datasets and model architectures (ICLR 2024, 273+ stars, MIT License)
- Protein Data Bank - Protein structures
- ChEMBL - Chemical bioactivity data
- Human Protein Atlas - Protein expression data
- Chinese Medical Dataset - Comprehensive collection of Chinese medical datasets for AI research
- Arc Virtual Cell Atlas - Curated open dataset collection of 602M+ observational and perturbational single-cell profiles for accelerating virtual cell model creation, integrating Tahoe-100M and scBaseCount data with Google Cloud Marketplace distribution (Arc Institute, 2025-2026)
Chemistry & Materials
Physics
- The Well - 15TB collection of 16 large-scale numerical simulation datasets spanning fluid dynamics, MHD, astrophysics, biological systems, and acoustic scattering, with unified PyTorch dataloaders and benchmarks for training foundation models on physical sciences (Polymathic AI, NeurIPS 2024)
- RealPDEBench (ICLR 2026 Oral) - First scientific ML benchmark with paired real-world measurements and matched numerical simulations for complex physical systems, featuring 5 scenarios, 700+ trajectories, 10 baseline models, and 9 evaluation metrics with HuggingFace datasets and model checkpoints (Westlake University, CC BY-NC 4.0)
- LIGO Open Science Center - Gravitational wave data
- Particle Data Group - Particle physics data
- OpenQuantumMaterials - Quantum materials data
💻 Computing Frameworks
Machine Learning
- PyTorch - Deep learning framework
- JAX - High-performance ML research
- TensorFlow - End-to-end ML platform
Scientific Computing
Scientific Machine Learning Frameworks
- SciML - Scientific machine learning ecosystem
- DifferentialEquations.jl - Multi-language suite for high-performance differential equation solving and scientific machine learning (3.0k+ stars)
- ModelingToolkit.jl - Acausal modeling framework for automatically parallelized scientific machine learning (1.5k+ stars)
- SciMLBenchmarks.jl - Scientific machine learning benchmarks & differential equation solvers
- NeuralPDE.jl - Physics-informed neural networks (PINNs) for solving partial differential equations (1.1k+ stars)
- DiffEqFlux.jl - Neural ordinary differential equations with O(1) backprop and GPU support (900+ stars)
- Optimization.jl - Unified interface for local, global, gradient-based and derivative-free optimization (800+ stars)
- PaddleScience - SDK & library for AI-driven scientific computing applications
- Tesseract Core (Pasteur Labs, SciPy 2025 / JOSS) - Universal components for differentiable scientific computing, packaging heterogeneous scientific tools into self-contained, portable, gradient-propagating components with auto-generated schemas, CLI/REST API/Python SDK interfaces, and reproducible deployment across local, cloud, and HPC environments (105+ stars, Apache 2.0)
- Flux.jl - Machine learning in Julia
Specialized Frameworks
- MDAnalysis - Molecular dynamics analysis
- Nano World Model - Minimalist, batteries-included repository for training video world models with diffusion-forcing, supporting long-horizon rollouts, 3D point-cloud generation, and model-predictive control with pretrained checkpoints (Simchowitz Lab, 700+ stars, MIT License, 2026)
- e3nn - Euclidean neural networks for arbitrary point transformations enabling E(3)-equivariant deep learning, foundational library for building geometry-aware neural networks in molecular dynamics, materials science, and physics
- MDtrajNet - Neural network foundation model that directly generates MD trajectories bypassing force calculations, accelerating simulations by up to 100× with equivariant Transformer architecture (2025)
- ASE - Atomic Simulation Environment for materials modeling
- PyMC - Probabilistic programming
- AI2BMD - Microsoft's AI-powered ab initio biomolecular dynamics simulation achieving quantum-mechanical accuracy for proteins with 10,000+ atoms, orders of magnitude faster than DFT using protein fragmentation and ML force fields (Nature 2024)
- OpenMM - High-performance molecular simulation toolkit
- DeePMD-kit - Deep learning package for many-body potential energy representation and molecular dynamics, achieving quantum-mechanical accuracy with classical MD efficiency (DeepModeling, Gordon Bell Prize 2020, 1.9k+ stars)
- TorchMD - End-to-end molecular dynamics engine built on PyTorch, enabling differentiable simulations with neural network potentials and GPU acceleration for machine learning-accelerated molecular dynamics (MIT License, 707+ stars)
🎓 Educational Resources
Courses & Tutorials
Open Access Educational Materials
📋 Paper Collections & Repositories
YouTube Channels
🏛 Research Communities
Conferences
Organizations
Online Communities
📚 Related Awesome Lists
This project builds upon and complements several excellent resources:
🎯 Specialized Collections
📊 Paper & Research Collections
🌟 Key Insights from These Collections
- Current Focus: Shift from tool-level assistance to autonomous scientific agents
- Emerging Trends: Multi-modal scientific models, self-improving research systems
- Research Gaps: Evaluation frameworks, ethical governance, human-AI collaboration
- Future Directions: Fully autonomous discovery cycles, robotic lab integration
🤝 Contributing
We welcome contributions! Please see our Contributing Guidelines for details.
How to Contribute
- Fork this repository
- Add your resource in the appropriate section
- Ensure the format matches existing entries
- Submit a pull request with a clear description
Contribution Guidelines
- Ensure the resource is actively maintained
- Include a brief, clear description
- Check for duplicates before adding
- Use proper markdown formatting
📄 License
This project is licensed under the MIT License - see the LICENSE file for details.
🙏 Acknowledgments
Special thanks to all researchers and developers pushing the boundaries of AI for Science. This list is inspired by the awesome community and the transformative potential of AI in scientific discovery.
Star ⭐ this repository if you find it helpful!
