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

非技术概述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，目前的电池存在重大限制，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，需要寿命更长、能储存更多能量的电池。一个有希望的解决方案是用金属锂取代电池负极中使用的石墨，这种金属锂有可能储存大约10倍的能量。然而，锂金属面临的一个主要挑战是树枝状结构的形成--树枝状的小结构会随着时间的推移而增长，可能会导致短路，导致电池失效。本项目旨在了解如何通过研究材料的机械性能和确定具有优越机械特性的电解液来防止树枝晶的形成。这项研究是在休斯顿大学进行的，这是一家主要的拉美裔服务机构，为扩大代表不足的群体的参与提供了肥沃的土壤。该项目将招收研究生和本科生，并在大数据、人工智能和计算材料科学这一与国民经济和科学进步高度相关的新的跨学科领域进行专业培训。技术总结该项目旨在发现具有定制机械性能的固体材料，用作金属锂负极全固态电池的电解液。由于固体材料具有优异的力学性能，用固态电解液取代商用锂离子电池中的液态电解液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，但还没有成功地识别出能够完全抑制树枝晶的固体材料。目前对枝晶抑制的认识存在几个空白，包括(I)对力学性能标准的理解有限，(Ii)缺乏工具来准确地探索固体材料的全部力学行为，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假设以弹性性质的方向依赖性为特征的机械各向异性在锂金属阳极和固体材料的固-固界面上起着重要的作用，因此可以用来设计抑制树枝晶形成的固体电解质。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，用于产生和分析材料的各向异性弹性性质，并研究它们对Li树枝晶形核和生长的影响。具体地说，该项目将通过(I)开发不确定性量化的机器学习模型来预测材料的全部弹性张量，从而预测材料的各向异性行为，以及(Ii)进行高通量筛选，以识别机械稳定的固态电解质，从而解决这一差距。这种由机器学习指导的含锂材料的计算机筛选是一种高效和有效的方法，可以确定有前途的候选人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智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.