LLMs guide iterative high-throughput experiments to construct the Co-Al-Ge ternary phase diagram at 900°C, with a domain-specific LLM and general-purpose LLM showing complementary strengths in phase discovery.
Self-driving labor atory for accelerated discovery of thin-film materials
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NIMS-OS is an open-source Python framework that orchestrates AI modules (Bayesian optimization, phase diagram construction) with robotic hardware (NAREE) to enable autonomous closed-loop materials exploration.
Bayesian optimization automates the scientific discovery cycle by modeling observations with surrogate models and using acquisition functions to select experiments that balance known information with new exploration.
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LLM-guided phase diagram construction through high-throughput experimentation
LLMs guide iterative high-throughput experiments to construct the Co-Al-Ge ternary phase diagram at 900°C, with a domain-specific LLM and general-purpose LLM showing complementary strengths in phase discovery.
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NIMS-OS: An automation software to implement a closed loop between artificial intelligence and robotic experiments in materials science
NIMS-OS is an open-source Python framework that orchestrates AI modules (Bayesian optimization, phase diagram construction) with robotic hardware (NAREE) to enable autonomous closed-loop materials exploration.
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Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial
Bayesian optimization automates the scientific discovery cycle by modeling observations with surrogate models and using acquisition functions to select experiments that balance known information with new exploration.