理论计算和人工智能在固态电解质研究中的最新应用:一篇评论
Mingwei Wu1, Zheng Wei2, Yan Zhao1
1The College of Materials Science and Engineering, Sichuan University, Chengdu 610065, China.
Nanomaterials (Basel, Switzerland)
|February 13, 2025
概括
理论计算和人工智能 (AI) 加快了全固态电池 (ASSB) 固态电解质 (SSE) 的发现. 这些方法可以预测性能并优化材料,克服低离子导电性和差的界面稳定性等挑战.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学的计算化学
- 电化学 电化学 电化学
背景情况:
- 固态电解质 (SSEs) 对全固态电池 (ASSB) 至关重要,但面临着局限性.
- 低离子导电性和差的界面稳定性阻碍了SSE的性能.
- 计算科学和人工智能为SSE开发提供了强大的虚拟工具.
研究的目的:
- 审查理论计算和人工智能在开发高性能SSEs中的应用.
- 突出结合计算和人工智能方法进行材料发现的协同策略.
- 预测加速ASSB的SSE发展的未来趋势.
主要方法:
- 密度函数理论 (DFT) 和分子动力学 (MD) 的结构,特性和运输.
- 机器学习 (ML) 和深度学习 (DL) 用于属性预测和界面模拟.
- 高通量选 (HTS) 与理论和人工智能方法相结合.
主要成果:
- 理论计算优化材料结构,分析离子运输动态.
- 人工智能方法可以有效地预测材料特性,并模拟界面行为.
- 结合DFT,MD,ML,DL和HTS的协同策略显示出独特的优势.
结论:
- 理论计算和人工智能的整合大大加快了SSEs的发现和优化.
- 这些先进的方法是克服SSE发展当前挑战的关键.
- 这种方法有望推动ASSB的工业应用.
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