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MatPilot: an LLM-enabled AI Materials Scientist under the Framework of Human-Machine Collaboration

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arxiv 2411.08063 v1 pith:AYW7UKHC submitted 2024-11-10 physics.soc-ph cond-mat.mtrl-scics.AI

Ziqi Ni , Yahao Li , Kaijia Hu , Kunyuan Han , Ming Xu , Xingyu Chen , Fengqi Liu , Yicong Ye
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Shuxin Bai
This is my paper · ORCID
classification physics.soc-phcond-mat.mtrl-scics.AI
keywords materialsmatpilotcapabilitiesscientistabilitiescollaborationexperimentalhuman-machine
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 5 Pith papers

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