"Exploring the frontiers of science through artificial intelligence."
This repository is a personal archive of research papers, and upcoming resources dedicated to the field of AI driven scientific discovery. It reflects my ongoing effort to explore the transformative potential of artificial intelligence in enabling machines to ask questions, test hypotheses, and contribute to the advancement of knowledge.
The collection is intended to serve as a resource for researchers, students, and enthusiasts who share a passion for understanding how Artificial Intelligence can augment the process of scientific inquiry.
My ongoing pursuit is to build a well organized public archive that supports accelerating scientific inquiry through AI.
A basic introductory version outlining the early vision and evolving strategy behind this initiative.
📄 Vision Document:
AI_Copilot_Scientific_Discovery_Roadmap.pdf
“This roadmap represents an evolving effort to conceptualize, design, and iterate toward autonomous AI systems capable of contributing to scientific inquiry.
It serves as a research vision document: open for refinement and future extension.”
- 🌌 Purpose
- 📂 Thematic Sections
- 🛠️ Contribute
- 🌐 Related Resources
- 📜 Acknowledgment & Ethics
- 📣 Join the Journey
- Towards an AI Co-Scientist
arXiv:2502.18864
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Symbolic Regression with a Learned Concept Library
arXiv:2409.09359 -
AI Feynman 2.0: Pareto-Optimal Symbolic Regression Exploiting Graph Modularity
arXiv:2006.10782 -
LLM-Feynman: Leveraging Large Language Models for Universal Scientific Formula and Theory Discovery
arXiv:2503.06512
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CodeScientist: End-to-End Semi-Automated Scientific Discovery with Code-based Experimentation
arXiv:2503.22708 -
SynPAT: A System for Generating Synthetic Physical Theories with Data
arXiv:2505.00878 -
AIGS: Generating Science from AI-Powered Automated Falsification
arXiv:2411.11910
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DISCOVERYWORLD: A Virtual Environment for Developing and Evaluating Automated Scientific Discovery Agents
arXiv:2406.06769 -
Explainable AI-Assisted Optimization for Feynman Integral Reduction
arXiv:2502.09544
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OpenTensor: Reproducing Faster Matrix Multiplication Discovering Algorithms
arXiv:2405.20748 -
Inferring the Structure of Ordinary Differential Equations
arXiv:2107.07345 -
$\mathbf{X X^{\top}}$ Can Be Faster
arXiv:2505.09814🔬 Materials Science
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Toward Greater Autonomy in Materials Discovery Agents: Unifying Planning, Physics, and Scientists
arXiv:2506.05616 -
Adaptive AI Decision Interface for Autonomous Electronic Material Discovery
arXiv:2504.13344 -
Active Learning for Conditional Inverse Design with Crystal Generation & Foundation Atomic Models
arXiv:2502.16984 -
AI-driven materials design: a mini-review
arXiv:2502.02905 -
Explainable Multimodal Machine Learning for Revealing Structure–Property Relationships in Carbon Nanotube Fibers
arXiv:2502.07400
- Genesis: Towards the Automation of Systems Biology Research
arXiv:2408.10689
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Call for Action: Towards the Next Generation of Symbolic Regression Benchmark (SRBench)
arXiv:2505.03977 -
LLM-SRBench: A New Benchmark for Scientific Equation Discovery with Large Language Models
arXiv:2504.10415
- Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges
arXiv:2412.11427v2
- Rich Robot Behaviors from Interacting, Trusted LLMs
arXiv:2412.18588
This list will expand over time as new advancements are made and shared by the scientific community.
Although this is an ongoing initiative, contributions are welcome from anyone who shares an interest in advancing AI's role in science.
- Suggest Research Papers:
If you know of relevant works that should be included in this collection, feel free to open a Pull Request or Issue.
- Kaggle: Share your suggestions in the notebook comments.
- Highlight Gaps:
If you notice any underexplored areas or missing resources,etc please share your thoughts to make this repository more comprehensive.
This ongoing initiative aims to continuously grow and evolve, and your contributions will help shape its future.
For those looking to explore beyond this repository, here are some additional resources:
- Coming soon, stay tuned.
This repository is inspired by the collective contributions of researchers worldwide. Each work included here represents the effort and dedication of individuals and teams who have generously shared their findings for the benefit of the broader community.
I am committed to maintaining the integrity of this resource and ensuring that it contributes positively to the advancement of science. While Artificial Intelligence offers incredible potential, I recognize the ethical responsibilities involved in its application and encourage all contributors and users to approach this work with care and thoughtfulness.
This notebook repository is evolving as its a preliminary version: expect updates, refinements, and expansions as new research emerges, new and old papers exploration and the field advances. Everything here connects to the singular aim of accelerating scientific discovery through intelligent systems..
🌟 Follow this repository for updates.
🌟 Collaborate to expand and improve this archive.
🌟 Explore the possibilities at the intersection of Artificial Intelligence and Science.