Modeling Long-Time and Macroscopic Behavior of Complex Atomistic Systems with Application to Silicon-based Lithium Batteries

复杂原子系统的长期宏观行为建模及其在硅基锂电池中的应用

• 批准号: 1436950
• 负责人: Michael Ortiz
• 金额: $ 32.97万
• 依托单位: California Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1436950&HistoricalAwards=false
• 关键词: Modeling; Long; Time; Macroscopic; Behavior

In a number of areas of application, the behavior of systems depends sensitively on properties that pertain to the atomistic scale, i.e., the angstrom and femtosecond scales. However, often the properties and behaviors of interest are macroscopic and take place on the scale of centimeters to meters, and are characterized by slow evolution on the scale of minutes to years. No computationally-tractable atomistically-based models appear to be as yet available to study such slow phenomena over time scales of the order of minutes to years and in macroscopic samples while maintaining a strictly atomistic description of the material. This project addresses this chronic gap in predictive science. This approach offers unprecedented capability for the study of device-level properties mediated by slow, coupled, thermal-mechanical-chemical processes at the atomistic scale. Thus, beyond this application to Li-ion batteries, this methodology may be expected to have far-reaching impact as an enabling tool in applications requiring the careful accounting of atomic-level processes simultaneously with the elucidation of macroscopic properties over long time scales, e.g., stability of alloys and irradiated materials, electromigration in interconnects, corrosion and environmentally-assisted cracking, among others. The empirical atomic-level kinetic models, variational meanfield approximation schemes, variational time-discretization algorithms and spatial coarse-graining schemes developed under the project will be implemented into a verified and validated high-performance computing solver, the Extended Quasicontinuum (XQC) solver, for broad dissemination in the community.This work is concerned with the further development and implementation of a novel multiscale analysis methodology. It combines elements of non-equilibrium statistical mechanics and kinetic and approximation theory. This approach offers unprecedented capability for the study of the long-term macroscopic behavior of complex multi-species atomistic systems mediated by slow, coupled, thermal-mechanical-chemical processes at atomistic scales. Application of the novel methodology to the investigation of silicon lithiation, both in bulk and in nanowires (SiNW) is considered. The potential use of silicon as a high energy-density anode material in Li-ion based batteries is hampered by the extensive mechanical degradation that occurs during lithiation. The current global market size for such storage, just for vehicle applications, is estimated at $1.5 billion, and is expected to grow to by more than 3,000% by the end of the decade. The ability to simulate silicon lithiation predictively at the device level and over large numbers of charge/discharge cycles is expected to enable the identification and assessment of novel nano-engineered materials for Li battery applications.

在许多应用领域中，系统的行为敏感地依赖于属于原子尺度的特性，即埃和飞秒尺度。然而，我们感兴趣的性质和行为往往是宏观的，发生在厘米到米的尺度上，并且以分钟到年的缓慢演变为特征。目前还没有计算上易于处理的基于原子的模型，可以在保持材料的严格原子描述的情况下，在几分钟到几年的时间尺度上研究这种缓慢的现象。这个项目解决了预测科学中这个长期存在的差距。这种方法为在原子尺度上研究由缓慢的、耦合的、热-机械-化学过程介导的器件级性质提供了前所未有的能力。因此，除了在锂离子电池上的应用之外，这种方法可能会在需要仔细计算原子水平过程的同时，在长时间尺度上阐明宏观特性的应用中产生深远的影响，例如合金和辐照材料的稳定性、互连中的电迁移、腐蚀和环境辅助开裂等。该项目开发的经验原子级动力学模型、变分平均场近似方案、变分时间离散算法和空间粗粒度方案将被实现为一个经过验证和验证的高性能计算求解器，即扩展准连续体（XQC）求解器，以便在社区中广泛传播。这项工作涉及到一种新的多尺度分析方法的进一步发展和实施。它结合了非平衡统计力学、动力学和近似理论的元素。这种方法为在原子尺度上研究由缓慢、耦合、热-机械-化学过程介导的复杂多物种原子系统的长期宏观行为提供了前所未有的能力。考虑了该方法在硅锂化研究中的应用，包括块状和纳米线。硅在锂离子电池中作为高能量密度负极材料的潜在用途受到锂化过程中发生的广泛机械降解的阻碍。目前，仅车载储能系统的全球市场规模估计为15亿美元，预计到2020年将增长3000%以上。在设备级和大量充放电循环中预测硅锂化的能力有望使锂电池应用的新型纳米工程材料的识别和评估成为可能。