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.

锂离子电池在我们的日常生活中发挥着越来越重要的作用，为智能手机、平板电脑和电动汽车等设备提供动力。然而，目前的电池具有主要的局限性，例如安全问题和需要频繁充电。为了满足日益增长的储能需求，需要能够存储更多能量的更持久的电池。一个有希望的解决方案是用锂金属取代电池负极中使用的石墨，锂金属有可能储存大约10倍的能量。然而，锂金属的一个主要挑战是树枝状小树枝状结构的形成，随着时间的推移会导致短路，导致电池故障。本项目旨在通过研究材料的机械性能和识别具有上级机械特性的电解质来了解如何防止枝晶形成。这项研究是在休斯顿大学进行的，这是一个主要的西班牙裔服务机构，为扩大代表性不足的群体的参与提供了肥沃的土壤。本项目将招收研究生和本科生，在大数据、人工智能和计算材料科学这一与国家经济和科技发展高度相关的新的交叉学科领域进行专业培训。技术概述本项目旨在发现具有定制机械性能的固体材料，用于锂金属阳极全固态电池的电解质。由于固体材料的上级机械性能，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枝晶的最有前途的方法。然而，尽管进行了广泛的研究工作，但还没有成功鉴定出可以完全抑制树突的固体材料。目前对枝晶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）缺乏准确探测固体材料完整机械行为的工具，以及（iii）缺乏系统的方法来识别新的固体材料作为候选电解质。基于最近的理论和实验工作，PI假设机械各向异性，其特征在于弹性性能的方向依赖性，在锂金属阳极和固体材料之间的固-固界面上起着重要作用，因此可以利用它来设计抑制枝晶形成的固态电解质。该项目采用了材料基因组计划（MGI）的原则，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，并研究它们对Li枝晶成核和生长的影响。具体而言，该项目将通过以下方式解决这些差距：（i）开发不确定性量化的机器学习模型，以预测材料的完整弹性张量和各向异性行为;（ii）进行高通量筛选，以识别机械稳定的固态电解质。这种机器学习引导的含锂材料的计算筛选是一种高效和有效的方法，可以识别有前途的候选材料进行进一步的实验验证。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。