LEAPS-MPS: Machine Learning-guided Identification of Mechanically Stabilizing Solid-state Electrolytes

LEAPS-MPS：机器学习引导的机械稳定固态电解质的识别

• 批准号: 2316667
• 负责人: Mingjian Wen
• 金额: $ 24.99万
• 依托单位: University of Houston
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2316667&HistoricalAwards=false
• 关键词: LEAPS; MPS; Machine; Learning; guided

NON-TECHNICAL SUMMARY Lithium-ion batteries are playing an increasingly important role in our daily lives, powering devices like smartphones, tablets, and electric vehicles. Current batteries, however, have major limitations such as safety issues and the need for frequent recharging. To meet the growing demand for energy storage, longer-lasting batteries that can store more energy are needed. A promising solution is to replace the graphite used in the negative electrode of batteries with lithium metal, which has the potential to store about 10 times more energy. However, a major challenge with lithium metal is the formation of dendrites—small, branch-like structures that grow over time and can cause short circuits, leading to battery failure. This project aims to understand how to prevent dendrite formation by studying the mechanical properties of materials and identifying electrolytes with superior mechanical characteristics. The research is conducted at the University of Houston, a major Hispanic-Serving Institution, which provides a fertile ground for broadening participation from underrepresented groups. Graduate and undergraduate students will be recruited for this project and professionally trained in the new cross-disciplinary area of big data, artificial intelligence, and computational materials science, which is highly relevant to national economic and scientific advancement.TECHNICAL SUMMARYThis project aims to discover solid materials with tailored mechanical properties to be used as electrolytes in all-solid-state batteries with lithium metal anode. Replacing the liquid electrolyte in commercial Li-ion batteries with solid-state electrolytes is considered the most promising approach to suppress dendrites due to the superior mechanical properties of solid materials. However, despite extensive research efforts, no solid material that can completely suppress dendrites has been successfully identified. There are several gaps in the current understanding of dendrite suppression, including (i) limited understanding of the criteria on mechanical properties, (ii) lack of tools to accurately probe the full mechanical behaviors of solid materials, and (iii) lack of a systematic approach to identifying new solid materials as candidate electrolytes. Based on recent theoretical and experimental work, the PI hypothesizes that mechanical anisotropy, characterized by the directional dependence of elastic properties, plays a significant role at the solid-solid interface between Li metal anode and a solid material, and can thus be leveraged to design solid-state electrolytes that suppress the formation of dendrites. The project embraces the principles of the Materials Genome Initiative (MGI) and establishes a unique data-driven approach for the production and analysis of anisotropic elastic properties of materials and the investigation of their effects on Li dendrite nucleation and growth. Specifically, the project will address the gaps by (i) developing uncertainty-quantified machine learning models to predict the full elastic tensors and thus anisotropic behaviors of materials and (ii) conducting high-throughput screening to identify mechanically stabilizing solid-state electrolytes. This machine learning-guided computational screening of Li-containing materials is an efficient and effective approach to identifying promising candidates for further experimental verification.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

非技术摘要锂离子电池在我们的日常生活中发挥了越来越重要的作用，智能手机，平板电脑和电动汽车等设备供电。但是，当前的电池有主要限制，例如安全问题以及经常充电的需求。为了满足不断增长的储能需求，需要持续的电池，可以存储更多能量。一个有望的解决方案是用锂金属代替电池负电极中使用的石墨，该石金有可能增加能量的10倍。但是，锂金属的主要挑战是树突的形成，即随着时间的流逝而生长并可能导致短路，导致电池故障。该项目旨在通过研究材料的机械性能并鉴定具有优越的机械特性的电解质来了解如何防止树突形成。这项研究是在休斯敦大学的一家主要西班牙裔美国人服务机构进行的，该机构为扩大代表性不足的群体的参与提供了肥沃的立场。该项目将招募研究生和本科生，并在新的跨学科领域进行了专业培训，该项目，人工智能和计算材料科学与国家经济和科学进步高度相关。技术总结项目旨在将固体材料与全元素的固定材料一起使用。用固态电解质代替商用锂离子电池中的液体电解质被认为是抑制树突的最有前途的方法，这是由于固体材料的出色机械性能。但是，尽管进行了广泛的研究工作，但仍未成功地识别出可以完全抑制树突的固体材料。当前对树突抑制的理解存在几个差距，包括（i）对机械性能标准的理解有限，（ii）缺乏精确探测固体材料的全部机械行为的工具，以及（iii）缺乏将新的固体材料识别为候选电解质的系统方法。基于最近的理论和实验工作，PI假设以弹性性能的定向依赖性为特征的机械各向异性在Li金属阳极和固体物质之间的固相界面上起着重要作用，因此可以将其杠杆化至抑制树突形成的固态电解质。该项目涵盖了材料基因组倡议（MGI）的原理，并建立了一种独特的数据驱动方法，用于生产和分析材料的各向异性弹性特性，并研究其对LI Dendrite核化和生长的影响。具体而言，该项目将通过（i）开发不确定性定量的机器学习模型来预测材料的各向异性行为以及（ii）进行高通量筛选以识别机械稳定固态电解质的差异。这种机器学习指导的含Li材料的计算筛选是一种有效的方法，可以确定进一步实验验证的承诺。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子和更广泛的影响来审查标准，通过评估来诚实地支持支持。