【讲座问题】人工智能+理论驱动框架高效设计功效质料

【时 间】2024年6月24日 周一 上午8:30

【地 点】保定校区 动力工程系 教五楼102

【主讲人】李昊
，日本东北大学 质料科学高等研究所（WPI-AIMR）, 副教授

【主讲人简介】

李昊
，副教授
，2022年起任职于日本东北大学 (Tohoku University) 质料科学高等研究所 (WPI-AIMR)
，建设“数字催化及电池实验室 (DigCat & DigBat)”
，作为课题组认真人从事质料设计与盘算、人工智能 (AI和数据科学) 开发研究

。2014年至今已揭晓论文200余篇
，包括Nature Catalysis、Nature Communications、Journal of the American Chemical Society、Advanced Materials、ACS Catalysis、德国应化、Chemical Science等领域权威杂志

。

【讲座内容简介】

The design of solid-state materials is essential for a sustainable future. However, conventional materials search sometimes relies on the trial-and-error process from experiments. Meanwhile, the intricate structure-performance relationships of materials usually hamper the development of an effective design guideline. This talk will discuss an avenue to realize a data-driven framework for materials design combining artificial intelligence, materials theory, computational methodology development, and experiments. In particular, we will discuss i) how to reduce the complexity in catalyst design by materials theory and ii) how to develop new computational methods (i.e., package, on-the-cloud platform, model, and algorithm) to accelerate materials simulation. This talk will show the predictive power of theory in electrochemical and thermal catalysis, solid-state battery electrolytes, and hydrogen storage materials. We will also discuss the successful design of an “electron-refinery” strategy by transforming high-temperature thermal catalysis into low-temperature electrocatalysis. Finally, we will discuss the practical design of materials fusing artificial intelligence, materials theory, computational screening, computational methodology development, and experiments.