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Workshop: Adaptive Experimental Design and Active Learning in the Real World

Accelerated High-Entropy Alloys Discovery for Electrocatalysis via Robotic-Aided Active Learning

Zhichu Ren · Zhen Zhang · Yunsheng Tian · Ju Li


This work explores the accelerated discovery of High-Entropy Alloys electrocatalysts using a novel carbothermal shock fabrication method, underpinned by an active learning approach. A high-throughput robotic platform, integrating a BoTorch-based active learning module with an Opentrons liquid handling robot and a 7-axis robotic arm, expedites the iterative experimental cycles. The recent integration of large language models leverages ChatGPT’s API, facilitating voice-driven interactions between researchers and the automation setup, further enhancing the autonomous workflow under experimental materials science scenarios. Initial optimization efforts for green hydrogen production catalyst yield promising results, showcasing the efficacy of the active learning framework in navigating the complex materials design space of HEAs. This study also emphasizes the crucial need for consistency and reproducibility in real-world experiments to fully harness the potential of active learning in materials science explorations.

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