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| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.13344 |
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| _version_ | 1866915249874010112 |
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| author | Dai, Yahao Chan, Henry Vriza, Aikaterini Kim, Fredrick Wang, Yunfei Liu, Wei Shan, Naisong Xu, Jing Weires, Max Wu, Yukun Cao, Zhiqiang Miller, C. Suzanne Divan, Ralu Gu, Xiaodan Zhu, Chenhui Wang, Sihong Xu, Jie |
| author_facet | Dai, Yahao Chan, Henry Vriza, Aikaterini Kim, Fredrick Wang, Yunfei Liu, Wei Shan, Naisong Xu, Jing Weires, Max Wu, Yukun Cao, Zhiqiang Miller, C. Suzanne Divan, Ralu Gu, Xiaodan Zhu, Chenhui Wang, Sihong Xu, Jie |
| contents | AI-powered autonomous experimentation (AI/AE) can accelerate materials discovery but its effectiveness for electronic materials is hindered by data scarcity from lengthy and complex design-fabricate-test-analyze cycles. Unlike experienced human scientists, even advanced AI algorithms in AI/AE lack the adaptability to make informative real-time decisions with limited datasets. Here, we address this challenge by developing and implementing an AI decision interface on our AI/AE system. The central element of the interface is an AI advisor that performs real-time progress monitoring, data analysis, and interactive human-AI collaboration for actively adapting to experiments in different stages and types. We applied this platform to an emerging type of electronic materials-mixed ion-electron conducting polymers (MIECPs) -- to engineer and study the relationships between multiscale morphology and properties. Using organic electrochemical transistors (OECT) as the testing-bed device for evaluating the mixed-conducting figure-of-merit -- the product of charge-carrier mobility and the volumetric capacitance (μC*), our adaptive AI/AE platform achieved a 150% increase in μC* compared to the commonly used spin-coating method, reaching 1,275 F cm-1 V-1 s-1 in just 64 autonomous experimental trials. A study of 10 statistically selected samples identifies two key structural factors for achieving higher volumetric capacitance: larger crystalline lamellar spacing and higher specific surface area, while also uncovering a new polymer polymorph in this material. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_13344 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Adaptive AI decision interface for autonomous electronic material discovery Dai, Yahao Chan, Henry Vriza, Aikaterini Kim, Fredrick Wang, Yunfei Liu, Wei Shan, Naisong Xu, Jing Weires, Max Wu, Yukun Cao, Zhiqiang Miller, C. Suzanne Divan, Ralu Gu, Xiaodan Zhu, Chenhui Wang, Sihong Xu, Jie Materials Science Artificial Intelligence AI-powered autonomous experimentation (AI/AE) can accelerate materials discovery but its effectiveness for electronic materials is hindered by data scarcity from lengthy and complex design-fabricate-test-analyze cycles. Unlike experienced human scientists, even advanced AI algorithms in AI/AE lack the adaptability to make informative real-time decisions with limited datasets. Here, we address this challenge by developing and implementing an AI decision interface on our AI/AE system. The central element of the interface is an AI advisor that performs real-time progress monitoring, data analysis, and interactive human-AI collaboration for actively adapting to experiments in different stages and types. We applied this platform to an emerging type of electronic materials-mixed ion-electron conducting polymers (MIECPs) -- to engineer and study the relationships between multiscale morphology and properties. Using organic electrochemical transistors (OECT) as the testing-bed device for evaluating the mixed-conducting figure-of-merit -- the product of charge-carrier mobility and the volumetric capacitance (μC*), our adaptive AI/AE platform achieved a 150% increase in μC* compared to the commonly used spin-coating method, reaching 1,275 F cm-1 V-1 s-1 in just 64 autonomous experimental trials. A study of 10 statistically selected samples identifies two key structural factors for achieving higher volumetric capacitance: larger crystalline lamellar spacing and higher specific surface area, while also uncovering a new polymer polymorph in this material. |
| title | Adaptive AI decision interface for autonomous electronic material discovery |
| topic | Materials Science Artificial Intelligence |
| url | https://arxiv.org/abs/2504.13344 |