自适应AI界面提升电子材料发现效率，64次实验实现性能翻倍
Adaptive AI decision interface for autonomous electronic material discovery
- 构建实时反馈的AI助手，动态调整实验策略
- μC*提升150%，达1,275 F cm⁻¹ V⁻¹ s⁻¹，仅需64次试验
- 揭示结晶层间距与比表面积对电容的关键作用
基于人工智能的自主实验（AI/AE）虽可加速材料发现，但在电子材料领域受限于长周期设计-制备-测试-分析循环导致的数据稀缺。现有先进算法缺乏人类科学家般的实时适应能力。为此，我们在AI/AE系统中开发并部署了自适应AI决策接口，核心为能实时监控进度、分析数据并支持人机协同的AI顾问，动态响应不同实验阶段与类型。该平台应用于新型混合离子-电子导电聚合物（MIECPs），通过有机电化学晶体管（OECT）评估混合导电性能指标μC*（载流子迁移率与体积电容乘积）。在仅64次自主实验后，μC*相比常用旋涂法提升150%，达到1,275 F cm⁻¹ V⁻¹ s⁻¹。对10个统计样本的研究揭示：更大的结晶层间距与更高的比表面积是提升体积电容的关键因素，并发现一种新型聚合物多晶型。
原文摘要 · Abstract (English)
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.
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