A curated list of awesome AI tools, libraries, papers, datasets, and frameworks that accelerate scientific discovery across all disciplines.
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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.
- 🧪 AI Tools for Research
- 📄 Paper→Poster / Slides / Graphical Abstract
- 📊 Chart Understanding & Generation
- 🔄 Paper-to-Code & Reproducibility
- 📋 Scientific Documentation & Parsing
- 🧰 Research Workbench & Plugins
- 🕸️ Knowledge Extraction & Scholarly KGs
- 🤖 Research Agents & Autonomous Workflows
- 🏷️ Data Labeling & Curation
- ⚗️ Scientific Machine Learning
- 📖 Papers & Reviews
- 🔬 Domain-Specific Applications
- 🤖 Foundation Models for Science
- 📈 Datasets & Benchmarks
- 💻 Computing Frameworks
- 🎓 Educational Resources
- 🏛 Research Communities
- 📚 Related Awesome Lists
- 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
- 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
- Label Studio - Multi-type data labeling and annotation tool
- Snorkel - Programmatic data labeling and weak supervision
- Paper2Poster - Multi-agent system with Parser-Planner-Painter architecture converting
paper.pdfto editableposter.pptx, outperforms GPT-4o with 87% fewer tokens - mPLUG-PaperOwl - Multimodal LLM for scientific charts and diagrams understanding/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
- Paper2Video - First benchmark for automatic video generation from scientific papers (NeurIPS 2025)
- paper2video - Transform arXiv research papers into engaging presentations and YouTube-ready videos
- Paper2All - AI-powered pipeline converting papers into interactive websites, posters, and multimedia presentations with "Let's Make Your Paper Alive!" philosophy
Note: For comprehensive chart understanding and code generation tools, see 📊 Chart Understanding & Generation section
- ChartCoder (ACL 2025) - Multimodal LLM for chart-to-code generation, 7B model outperforms larger open-source MLLMs
- ChartAssistant / ChartAst (ACL 2024) - Universal chart comprehension and reasoning model
- Chart-to-Text Datasets - Large-scale chart summarization datasets for training chart description capabilities
- Chat2Plot - Secure text-to-visualization through standardized chart specifications
- AutoViz - Automated data visualization with minimal code
- PlotlyAI - AI-powered data visualization and dashboard creation
- 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
- BioProBench - Comprehensive benchmark for automatic evaluation of LLMs on biological protocols and procedural understanding
- Alhazen - Extract experimental metadata and protocol information from scientific documents
- MinerU (2024/2025) - SOTA multimodal document parsing with 1.2B parameters outperforming GPT-4o, converts PDFs to LLM-ready Markdown/JSON
- 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
- 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
- Marker - High-accuracy PDF→Markdown/JSON/HTML conversion, specialized for tables/formulas/code blocks with benchmark scripts
- S2ORC doc2json (AllenAI) - Large-scale PDF/LaTeX/JATS parsing to standardized JSON for millions of papers
- GROBID - Machine learning software for extracting structured metadata from scholarly documents
- Science-Parse / SPv2 (AllenAI) - Parse scientific papers to structured fields (title/author/sections/references)
- IBM Data Prep Kit: PDF→Parquet - Large-scale scientific document ingestion pipeline with optimization configurations
- Mozilla document-to-markdown - Docling-powered parsing with UI/CLI demonstration for rapid prototyping
- PDFFigures2 - Extract figures, tables, captions, and section titles from scholarly PDFs
- TableBank - Large-scale table detection and recognition dataset with pre-trained models
- PaperQA2 - High-accuracy RAG for scientific PDFs with citation support, agentic RAG, and contradiction detection
- paper-reviewer - Generate comprehensive reviews from arXiv papers and convert to blog posts
- Jupyter AI (JupyterLab Extension) - Official Jupyter extension with
%%aimagic 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
- 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
- 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
- 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)
- Awesome-LLM-KG - Comprehensive collection of papers on unifying LLMs and knowledge graphs
- The AI Scientist v1 (2024) - First fully autonomous research system: hypothesis→experiment→writing→review simulation
- The AI Scientist v2 (2025) - Enhanced with Agentic Tree Search, reduced template dependency, first workshop-level accepted paper
- DeepScientist - First system progressively surpassing human SOTA on frontier AI tasks (183.7%, 1.9%, 7.9% improvements), month-long autonomous discovery with 20,000+ GPU hours
- Kosmos - Extended autonomy AI scientist with 200 parallel agent rollouts, 42K lines of code execution, 1.5K papers analyzed per run, achieving 79.4% accuracy and 7 scientific discoveries (Edison Scientific)
- AlphaResearch - Autonomous algorithm discovery combining evolutionary search with peer-review reward models, achieving best-known performance on circle packing problems
- AI-Researcher - Autonomous pipeline from literature review→hypothesis→algorithm implementation→publication-level writing with Scientist-Bench evaluation
- Agent Laboratory - Multi-agent workflows for complete research cycles with AgentRxiv for cumulative discovery
- InternAgent - Closed-loop multi-agent system from hypothesis to verification across 12 scientific tasks, #1 on MLE-Bench (36.44%)
- freephdlabor - First fully customizable open-source multiagent framework automating complete research lifecycle from idea conception to LaTeX papers with dynamic workflows
- ToolUniverse - Democratizing AI scientists by transforming any LLM into research systems with 600+ scientific tools (Harvard MIMS)
- Aviary - Language agent gymnasium for challenging scientific tasks including DNA manipulation, literature search, and protein engineering
- Curie - Automated and rigorous experiments using AI agents for scientific discovery
- POPPER - Automated hypothesis testing with agentic sequential falsifications
- ScienceAgentBench (ICLR 2025) - 102 executable tasks from 44 peer-reviewed papers across 4 disciplines with containerized evaluation
- SciTrust (2024) - Trustworthiness evaluation framework for scientific LLMs (truthfulness, hallucination, sycophancy)
- SciBench - College-level scientific problem-solving evaluation across multiple domains
- AgentReview - LLM agents simulating academic peer review ecosystems
- LLM-Peer-Review - Web application for LLM-assisted manuscript review and annotation
- BioDiscoveryAgent - AI agent for biological discovery and research automation
- MOOSE - Large Language Models for automated open-domain scientific hypotheses discovery (ACL 2024, ICML Best Poster)
- ChemCrow - LLM agents for chemistry research with tool integration
- Coscientist - Autonomous chemical experiment planning and execution
- Snorkel - Programmatic data labeling and weak supervision for scientific datasets
- PandasAI - Conversational data analysis and visualization using natural language
- torchdiffeq - PyTorch implementation of neural ODEs
- torchdyn - Neural differential equations in PyTorch
- diffrax - Numerical differential equation solving in JAX
- DifferentialEquations.jl - Julia differential equations suite
- DiffEqFlux.jl - Neural differential equations in Julia
- DeepXDE - Deep learning library for solving PDEs
- 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
- SciANN - Keras-based scientific neural networks
- NeuralPDE.jl - Physics-informed neural networks in Julia
- DeepONet - Learning nonlinear operators
- PySINDy - Sparse identification of nonlinear dynamics
- Fourier Neural Operator - Learning operators in Fourier space
- Machine Learning for Scientometric Analysis (2021.09) - Comprehensive review
- AI for Science: Progress and Challenges (2023.03) - State of the field
- Foundation Models for Science (2022.05) - Large models in research
- Neural Ordinary Differential Equations (2018.06) - Breakthrough in neural ODEs
- Physics-Informed Neural Networks (2017.11) - Physics-constrained deep learning
- Scientific Discovery in the Age of Artificial Intelligence - Nature review on AI's role in science
- A Survey on AI-assisted Scientific Discovery (2025.02) - Comprehensive overview of LLMs in scientific research lifecycle from literature search to peer review
- AI4Research: A Survey of Artificial Intelligence for Scientific Research (2025.07) - Systematic taxonomy of AI in research
- Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems (2023.07) - Unified technical survey across scientific scales with 63 contributors
- From Automation to Autonomy: A Survey on Large Language Models in Scientific Discovery (2025.05) - Three-level taxonomy (Tool, Analyst, Scientist)
- From AI for Science to Agentic Science: A Survey on Autonomous Scientific Discovery (2025.08) - Comprehensive survey on agentic science across life sciences, chemistry, materials, and physics
- Agentic AI for Scientific Discovery: A Survey of Progress, Challenges, and Future Directions (2025.03) - Comprehensive review of AI agents in science
- Towards Scientific Intelligence: A Survey of LLM-based Scientific Agents (2025.03) - Scientific AI agent systems
- A Comprehensive Survey of Scientific Large Language Models and Their Applications (2024.06) - 260+ scientific LLMs across domains
- A Survey of Scientific Large Language Models: From Data Foundations to Agent Frontiers (2025.08) - Data-centric view of scientific LLMs
- Scientific Large Language Models: A Survey on Biological & Chemical Domains (2024.01) - Domain-specific scientific LLMs
- Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next (2022.01) - Comprehensive PINN review
- Physics-Informed Neural Networks and Extensions (2024.08) - Recent PINN advances and variants
- The frontier of simulation-based inference (PNAS 2020) - Foundational review on SBI for scientific computing by Cranmer et al.
- From Theory to Application: A Practical Introduction to Neural Operators in Scientific Computing (2025.03) - Implementation-focused guide to DeepONet, FNO, and PCANet
- Architectures, variants, and performance of neural operators: A comparative review (2025) - Systematic analysis of DeepONets, integral kernel operators, and transformer-based neural operators
- Foundation Models for Environmental Science: A Survey (2025.04) - Environmental applications
- Foundation Models in Bioinformatics - Biological foundation models
- Foundation Models for Materials Discovery (2025) - Perspective on materials AI
- Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons (J. Comput. Phys. 2023) - Comprehensive framework for UQ in PINNs and neural operators by Psaros et al.
- A Survey on Uncertainty Quantification Methods for Deep Learning (2023) - Systematic taxonomy of UQ methods from uncertainty source perspective
- Self-Driving Laboratories for Chemistry and Materials Science (Chem. Rev. 2024) - Comprehensive 100-page review on SDL technology, applications, and infrastructure
- Autonomous 'self-driving' laboratories: a review of technology and policy implications (Royal Soc. Open Sci. 2025) - Technology review with policy and safety considerations
- 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
- The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery (2024.08) - First fully autonomous research system
- The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search (2025.04) - Enhanced autonomous research with agentic tree search
- AI-Researcher: Autonomous Scientific Innovation (2025.05) - Autonomous research pipeline from literature to publication with Scientist-Bench evaluation framework
- InternAgent: When Agent Becomes the Scientist -- Building Closed-Loop System from Hypothesis to Verification (2025.05) - Multi-agent system achieving #1 on MLE-Bench with closed-loop research automation
- Autonomous Scientific Discovery Through Hierarchical AI Scientist Systems (2025.07) - Self-evolving multi-agent research systems
- ChemCrow: Augmenting large-language models with chemistry tools (2023.04) - LLM agents for chemistry research
- Autonomous chemical research with large language models - Automated chemical experimentation
- Coscientist: Autonomously planning and executing scientific experiments - Robotic lab automation
- AlphaFold: Protein Structure Prediction
- AI for Materials Discovery
- Large Language Models in Chemistry (2024.02)
- Cell2Sentence: Teaching Large Language Models the Language of Biology (ICML 2024) - LLMs for single-cell transcriptomics
- Scaling Large Language Models for Next-Generation Single-Cell Analysis (2025.04) - 27B parameter biological language models
- Boltz-1: Democratizing Biomolecular Interaction Modeling (bioRxiv 2024) - First fully open-source model achieving AlphaFold3-level accuracy
- MOOSE: Large Language Models for Automated Open-domain Scientific Hypotheses Discovery (ACL 2024) - First work showing LLMs can generate novel and valid scientific hypotheses, ICML Best Poster Award
- Earth-Agent: Unlocking the Full Landscape of Earth Observation with Agents (2025.09) - LLM agent framework for Earth Observation with 104 specialized tools and multi-modal analysis
- MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoning (ACL 2024) - Multi-disciplinary collaboration framework for medical reasoning using role-playing LLM agents
- MedAgentGym: A Scalable Agentic Training Environment for Code-Centric Reasoning in Biomedical Data Science (2025.06) - Specialized training environment for biomedical AI agents with code-centric reasoning
- Paper2Web: Let's Make Your Paper Alive! (2025.10) - AI-powered transformation of academic papers into interactive websites with comprehensive evaluation framework
- DeepAnalyze: Agentic Large Language Models for Autonomous Data Science (2025.10) - First agentic LLM for autonomous data science with curriculum-based training
- Democratizing AI scientists using ToolUniverse (2025.09) - Universal ecosystem for building AI scientists from any LLM with 600+ scientific tools
- TxAgent: An AI Agent for Therapeutic Reasoning Across a Universe of Tools (2025.03) - AI agent achieving 92.1% accuracy in drug reasoning, outperforming GPT-4o by 25.8%
- Aviary: Training Language Agents on Challenging Scientific Tasks (2024.12) - Language agent training framework for scientific discovery
- Galactica: A Large Language Model for Science (2022.11)
- ScienceAgentBench (ICLR 2025) - 102 executable tasks from 44 peer-reviewed papers across 4 disciplines with containerized evaluation
- Scientist-Bench - Comprehensive benchmark for comparing LLM Agent-generated research outcomes with high-quality scientific work
- SciTrust: Evaluating the Trustworthiness of Large Language Models for Science (2024) - Scientific LLM trustworthiness evaluation framework
- SciBench: Evaluating College-Level Scientific Problem-Solving Abilities (2023) - Scientific reasoning benchmarks
- ChartCoder Evaluation - Chart-to-code generation benchmarks
- AlphaFold - Protein structure prediction
- ColabFold (2025 Updates) - AlphaFold/ESMFold accessible implementation with AF3 JSON export, database updates
- Protenix - Trainable PyTorch reproduction of AlphaFold 3
- Boltz - First fully open-source model achieving AlphaFold3-level accuracy with 1000x faster binding affinity prediction (MIT)
- xfold - Democratizing AlphaFold3: PyTorch reimplementation to accelerate protein structure prediction research
- MegaFold - Cross-platform system optimizations for accelerating AlphaFold3 training with 1.73x speedup and 1.23x memory reduction
- Graphormer - General-purpose deep learning backbone for molecular modeling
- targetdiff - 3D Equivariant Diffusion for Target-Aware Molecule Generation (ICLR2023)
- DrugAssist - LLM-based molecular optimization tool
- mint - Learning the language of protein-protein interactions
- Mol-Instructions - Large-scale biomolecular instruction dataset for chemistry/biology LLMs (ICLR2024)
- ChemBERTa - Chemical language model
- DeepChem - Machine learning for chemistry
- DeepMol - Unified ML/DL framework for drug discovery workflows, integrating RDKit, DeepChem, and scikit-learn with SHAP explainability
- RDKit - Cheminformatics toolkit
- ESMFold - Protein structure prediction from ESM models
- LucaOne - Generalized biological foundation model with unified nucleic acid and protein language, integrating DNA/RNA/protein sequences (Nature Machine Intelligence 2025)
- scGPT - Single-cell analysis with transformers
- Cell2Sentence - Teaching Large Language Models the Language of Biology through single-cell transcriptomics (ICML 2024)
- Enformer - Gene expression prediction
- DNABERT - DNA sequence analysis
- scBERT - Single-cell BERT for gene expression
- GenePT - Generative pre-training for genomics
- MedAgents - Multi-disciplinary collaboration framework for zero-shot medical reasoning using role-playing LLM agents (ACL 2024)
- MedAgentGym - Scalable agentic training environment for code-centric reasoning in biomedical data science
- LLM4Chemistry - Curated paper list about LLMs for chemistry covering fine-tuning, reasoning, multi-modal models, agents, and benchmarks (COLING 2025)
- FAIRChem (OMat24) - Meta's comprehensive ML ecosystem for materials/chemistry with 118M+ DFT calculations, EquiformerV2 models achieving top Matbench Discovery performance
- MACE - Machine learning interatomic potentials
- MatterSim - Deep learning atomistic model across elements, temperatures, and pressures
- Crystal Graph CNNs - Crystal property prediction
- MatBench - Materials informatics benchmark
- Best of Atomistic Machine Learning - Curated list of atomistic ML projects for materials science
- Molecular Transformers - AI for chemical reaction prediction and synthesis planning
- JAX-MD - Molecular dynamics in JAX
- Neural ODEs - Differential equations with neural networks
- Physics-Informed Neural Networks - Physics-constrained ML
- EquiformerV2 - Improved equivariant Transformer for 3D atomic graphs (ICLR2024)
- Equiformer - Equivariant graph attention Transformer (ICLR2023)
- AstroPy - Python astronomy tools
- Gaia Archive - Stellar data for ML
- DeepSphere - Spherical CNNs for astronomy
- ClimaX - First foundation model for weather and climate by Microsoft, Vision Transformer-based architecture trained on heterogeneous datasets (ICML 2023)
- ClimateBench - Climate data benchmark for ML models
- WeatherBench - Weather prediction benchmark
- WeatherGFT - Physics-AI hybrid modeling for fine-grained weather forecasting (NeurIPS'24)
- Awesome Large Weather Models - Curated list of large weather models for AI Earth science
- TerraTorch - Python toolkit for fine-tuning geospatial foundation models
- Earth-Agent - LLM agent framework for Earth Observation with 104 specialized tools across 5 functional kits
- AI for Earth - Microsoft's environmental AI
- PlantNet - Plant identification using AI and citizen science
- AgML - Agricultural machine learning platform
- BioSimulators - Biological simulation tools
- EcoNet - Ecological modeling and conservation AI
- Galactica - Large language model for science
- MinervaAI - Mathematical reasoning
- PaLM-2 - Scientific reasoning capabilities
- ESM - Protein language models
- ChemGPT - Chemistry-focused language model
- BioGPT - Biomedical text generation
- Hugging Face Datasets - Comprehensive ML research datasets and scientific data collections
- Google Dataset Search - Find scientific datasets
- 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
- Materials Project - Computational materials database
- QM9 - Small molecule properties
- Open Catalyst Project - Catalyst discovery
- LIGO Open Science Center - Gravitational wave data
- Particle Data Group - Particle physics data
- OpenQuantumMaterials - Quantum materials data
- PyTorch - Deep learning framework
- JAX - High-performance ML research
- TensorFlow - End-to-end ML platform
- NumPy - Numerical computing
- SciPy - Scientific computing
- Scikit-learn - Machine learning library
- 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
- Flux.jl - Machine learning in Julia
- MDAnalysis - Molecular dynamics analysis
- ASE - Atomic Simulation Environment for materials modeling
- PyMC - Probabilistic programming
- OpenMM - High-performance molecular simulation toolkit
- AI for Everyone (Coursera) - Basic AI concepts
- CS229 Machine Learning - Stanford ML course
- MIT 6.034 Artificial Intelligence - AI fundamentals
- SciML Book - Parallel Computing and Scientific Machine Learning: MIT 18.337J/6.338J course materials (1.9k+ stars)
- Dive into Deep Learning - Interactive deep learning book with code implementations
- The Elements of Statistical Learning - Classic ML textbook freely available
- Neural Networks and Deep Learning - Free online book by Michael Nielsen
- Awesome Scientific Language Models - Curated scientific LLM papers (260+ models)
- Awesome LLM Scientific Discovery - LLM papers for scientific discovery
- AI4Research Papers - LLM for scientific research papers
- Physics-Informed Neural Networks Papers - PINN research collection
- Scientific Computing with ML Papers - Scientific ML paper repository
- Simulation-Based Inference Papers & Tools - Community-maintained SBI research portal with papers and software
- Awesome AI Scientist Papers - Autonomous AI scientist research
- Awesome Agents for Science - LLM agents across scientific domains
- Two Minute Papers - AI research summaries
- 3Blue1Brown - Mathematical concepts
- AI Coffee Break - AI paper reviews
- Steve Brunton - Data-driven methods
- Nathan Kutz - Applied mathematics
- Physics Informed Machine Learning - SciML tutorials
- NeurIPS - Machine learning conference
- ICML - International Conference on Machine Learning
- AI for Science Workshop - Specialized workshops
- Partnership on AI - AI research collaboration
- Allen Institute for AI - AI research institute
- OpenAI - AI research and deployment
- r/MachineLearning - ML discussions
- AI Alignment Forum - AI safety research
- Distill - Visual explanations of ML
This project builds upon and complements several excellent resources:
- awesome-ai4s - 200+ AI for Science papers with Chinese interpretations
- Awesome AI Scientist Papers - Autonomous AI scientist research
- Awesome Scientific Machine Learning - Physics-informed ML and SciML
- Awesome Agents for Science - LLM agents across scientific domains
- Awesome LLM Agents Scientific Discovery - Biomedical AI agents
- Awesome Foundation Models for Weather and Climate - Comprehensive survey of foundation models for weather and climate data understanding
- Scientific LLM Papers - 260+ scientific language models
- LLM4SR Repository - LLM for scientific research survey materials
- PINNs Paper Collection - Physics-informed neural networks research
- SciML Papers - Scientific computing and machine learning papers
- 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
We welcome contributions! Please see our Contributing Guidelines for details.
- Fork this repository
- Add your resource in the appropriate section
- Ensure the format matches existing entries
- Submit a pull request with a clear description
- Ensure the resource is actively maintained
- Include a brief, clear description
- Check for duplicates before adding
- Use proper markdown formatting
This project is licensed under the MIT License - see the LICENSE file for details.
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!
Last updated: October 2025 - Enhanced with 2024-2025 breakthroughs in autonomous research, document parsing, and scientific agents
