离子电导率
电解质
二价
扩散
化学物理
氢化物
离子键合
电导率
离子
电池(电)
快离子导体
锂(药物)
分子
化学
材料科学
热力学
物理化学
物理
电极
金属
有机化学
医学
功率(物理)
内分泌学
作者
Egon Campos dos Santos,Ryuhei Sato,Kazuaki Kisu,Kartik Sau,Xue Jia,Fangling Yang,Shin‐ichi Orimo,Hao Li
标识
DOI:10.1021/acs.chemmater.3c00975
摘要
The need for next-generation batteries is as urgent as ever. Over the past few decades, many attempts to find "beyond lithium" battery electrolytes have been reported, and, in particular, divalent closo-type complex hydride (CTCH) electrolytes are valuable alternatives to overcome the safety and energy density limitations of lithium-ion technology. Experiments have found that adding neutral molecules into the CTCH lattice can significantly promote its performance as battery electrolytes by accelerating cation conductivity (i.e., diffusion rate). However, the extremely high structural complexity of neutral molecules containing CTCHs hampers the exploration of ionic diffusion mechanisms and the design of high-performance batteries. To address this challenge, herein, cation diffusions of various CTCHs are analyzed by a workflow combining (i) a global optimization strategy based on a genetic algorithm, which will allow for identifying stable crystal phases of CTCHs, and (ii) ab initio kinetics and molecular dynamics simulations for cation diffusion. Without relying on any experimental information beforehand, this integrated strategy not only successfully predicts structural information that is comparable to experiments but also predicts almost identical diffusion activation energies compared to experimental observations. Based on these results, we developed robust structure-performance relationships that can precisely predict the divalent CTCH performance and identify the key factors that affect ionic conductivity. This study paves a new avenue for building a precise structure–performance picture of complex materials starting from near-zero information.
科研通智能强力驱动
Strongly Powered by AbleSci AI