AI4EC | Tian Research Group

AI4EC Vision

AI4EC brings artificial intelligence for science (AI4S) into physical chemistry, nanoscience, and electrochemistry. We combine advanced spectroscopy, theory and modeling, and nanostructure design to understand and engineer surfaces, nanostructures, and electrochemical interfaces from molecular to nanoscale dimensions.

Complex electrochemical systems involve coupled charge, energy, and mass transport across dynamic interfaces. AI4EC connects surface-enhanced spectroscopy, spectroelectrochemistry, plasmonics, catassembly, nanostructure synthesis, and theoretical modeling with AI-driven data analysis and instrumental automation, creating a route from observation to understanding and control.

AI4EC framework connecting advanced characterization and AI-enabled operando closed-loop research — AI4EC connects advanced characterization, physical chemistry, theory, and AI-enabled operando feedback to new electrochemical devices and systems.

From Operando Data to Intelligent Control

Operando spectroscopy and sensing provide time-resolved information on reaction intermediates, products, and interfacial evolution under working conditions. The AI4EC framework integrates these data streams with electrochemical parameters and their correlations, enabling rapid analysis and decisions while an experiment or device remains in operation.

Our aim is a closed loop of measurement, analysis, and control: operando measurements identify informative states, AI-assisted analysis summarizes the evolving system, and feedback adjusts working parameters to test hypotheses or improve performance. This approach can make complex electrochemical experiments more informative, efficient, and responsive.

AI-assisted operando measurement, analysis, and control cycle — AI links operando measurement, data analysis, and control through a real-time information flow.

AI-enabled feedback control for lithium batteries — Illustration of an AI-enabled feedback loop for operando monitoring and control in battery systems.

Towards Intelligent Electrochemical Discovery

AI4EC advances from AI assistance for existing techniques, through closed-loop optimization at the device level, toward the discovery of new principles, devices, and systems. The research direction explores intelligent instruments, multidimensional data frameworks, and coupled experiment-model workflows for complex electrochemical interfaces.

By connecting operando characterization, mechanistic understanding, and intelligent control, AI4EC seeks to reveal complex interfacial processes and accelerate the discovery of next-generation functional materials and energy systems.

Three levels of AI-enabled development for electrochemistry — AI-assisted enhancement, AI-enabled closed-loop optimization, and AI-driven creation form successive stages toward intelligent electrochemical systems.

AI4EC 愿景

AI4EC 将科学人工智能（AI4S）引入物理化学、纳米科学和电化学研究。课题组融合先进光谱、理论与建模以及纳米结构设计，从分子到纳米尺度理解并调控表面、纳米结构和电化学界面。

复杂电化学体系涉及动态界面上电荷、能量和物质传输的耦合。AI4EC 将表面增强光谱、谱学电化学、等离激元、催组装、纳米结构合成和理论建模，与 AI 驱动的数据分析和仪器自动化相结合，形成从观测、理解到调控的研究路径。

连接先进表征与 AI 工况闭环研究的 AI4EC 框架 — AI4EC 将先进表征、物理化学、理论与 AI 赋能的工况反馈相连接，面向新型电化学器件与系统。

从工况数据到智能控制

工况光谱和传感能够在真实工作条件下提供反应中间体、产物及界面演化的时间分辨信息。AI4EC 将这些数据流与电化学参数及其关联整合，使实验或器件运行过程中能够进行快速分析和决策。

我们的目标是建立“检测、解析、控制”的闭环：工况测量识别有信息量的状态，AI 辅助分析归纳体系的动态演化，反馈控制再调整工作参数以检验假设或提升性能。这一框架能够使复杂电化学实验更具信息量、效率和响应能力。

AI 辅助的工况测量、分析和控制循环 — AI 通过实时信息流连接工况测量、数据分析与控制。

用于锂电池的 AI 反馈控制 — 电池体系中工况监测与控制的 AI 反馈闭环示意。

面向智能电化学发现

AI4EC 从 AI 对既有技术的辅助增强出发，进一步开展器件层面的闭环优化，并朝向新原理、新器件和新系统的发现。该方向探索智能仪器、多维数据框架以及面向复杂电化学界面的实验与模型协同工作流。

通过连接工况表征、机理理解和智能控制，AI4EC 致力于揭示复杂界面过程，并加速下一代功能材料与能源体系的发现。