MatPilot: an LLM-enabled AI Materials Scientist under the Framework of Human-Machine Collaboration
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Abstract (arXiv)
The rapid evolution of artificial intelligence, particularly large language models, presents unprecedented opportunities for materials science research. We proposed and developed an AI materials scientist named MatPilot, which has shown encouraging abilities in the discovery of new materials. The core strength of MatPilot is its natural language interactive human-machine collaboration, which augments the research capabilities of human scientist teams through a multi-agent system. MatPilot integrates unique cognitive abilities, extensive accumulated experience, and ongoing curiosity of human-beings with the AI agents' capabilities of advanced abstraction, complex knowledge storage and high-dimensional information processing. It could generate scientific hypotheses and experimental schemes, and employ predictive models and optimization algorithms to drive an automated experimental platform for experiments. It turns out that our system demonstrates capabilities for efficient validation, continuous learning, and iterative optimization.
Key facts. Cited by 11 papers in the map (data through 13 Oct 2026). First posted on arXiv 10 Nov 2024.
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Citing papers per year: 2025: 6; 2026: 5.
11 papers.
- 2025Agentic AI for Scientific Discovery: A Survey of Progress, Challenges, and Future Directionscited by 77
- 2025From AI for Science to Agentic Science: A Survey on Autonomous Scientific Discoverycited by 76
- 2026Code as Agent Harnesscited by 44
- 2025Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systemscited by 14
- 2025Autonomous Agents for Scientific Discovery: Orchestrating Scientists, Language, Code, and Physicscited by 11
- 2025PriM: Principle-Inspired Material Discovery through Multi-Agent Collaborationcited by 7
- 2025A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Toolscited by 7
- 2026Self-Improvement of Large Language Models: A Technical Overview and Future Outlookcited by 5
- 2026S1-NexusAgent: a Self-Evolving Agent Framework for Multidisciplinary Scientific Researchcited by 3
- 2026HarmonyCell: Automating Single-Cell Perturbation Modeling under Semantic and Distribution Shiftscited by 3
- 2026Position: AI Agents in Scientific Teams Should Be Studied as Human-Agent Systemscited by 0
- 2026Position: AI Agents in Scientific Teams Should Be Studied as Human-Agent Systems2 Aug 2026
- 2026Code as Agent Harness18 May 2026
- 2026Self-Improvement of Large Language Models: A Technical Overview and Future Outlook26 Mar 2026
- 2026HarmonyCell: Automating Single-Cell Perturbation Modeling under Semantic and Distribution Shifts2 Mar 2026
- 2026S1-NexusAgent: a Self-Evolving Agent Framework for Multidisciplinary Scientific Research2 Feb 2026
- 2025Autonomous Agents for Scientific Discovery: Orchestrating Scientists, Language, Code, and Physics10 Oct 2025
- 2025From AI for Science to Agentic Science: A Survey on Autonomous Scientific Discovery18 Aug 2025
- 2025A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools25 Jun 2025
- 2025Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems23 May 2025
- 2025PriM: Principle-Inspired Material Discovery through Multi-Agent Collaboration9 Apr 2025
- 2025Agentic AI for Scientific Discovery: A Survey of Progress, Challenges, and Future Directions12 Mar 2025