報告題目：Facilitating Practical All-Solid-State Batteries with Computation and Data-Driven Methods

報告人：李昱亨博士 新加坡國立大學

主持人（邀請人）：賀欣、張立軍

報告時間：2024年6月27日 14:30-16:00

報告地點：唐敖慶D區429

主辦單位：伟德bv1946

汽車材料教育部重點實驗室

摘要：Rechargeable batteries play an essential role in sustainable energy to store intermittent green energy sources and power electric cars. All-solid-state batteries (ASSBs) hold promise as a safer and higher-energy-density alternative to commercial lithium-ion batteries. Computational materials science and machine learning can greatly accelerate the development of ASSBs by providing insights into their practical challenges. In this seminar, I will introduce how I applied computational approaches to the understanding and optimization of material properties, and how I utilized materials data from high-throughput calculations to design novel materials by developing physics-informed machine learning algorithms. The seminar will delve into the specific examples of solid-electrolyte materials regarding the issues of interfacial resistance and interphase formation. These advancements are crucial for realistically addressing the key challenges for cell performance and full-scale implementation of ASSBs.

報告人簡介：

Yuheng Li is an Eric and Wendy Schmidt AI in Science Fellow at the National University of Singapore. He obtained his PhD degree (2020) in NanoEngineering from University of California San Diego (UCSD), and his Bachelor’s degree (2015) from Zhejiang University. Yuheng specializes in computational and data-driven materials design for sustainable energy applications, with a particular focus on solid-state rechargeable-battery materials and hybrid halide perovskites. He has published eight first-author papers in high-impact journals such as Nature, Energy & Environmental Science, and PRX Energy. Additionally, he leads the development of materials database projects and serves as reviewer for reputable journals.