ERI: Learning the Constitutive Equations of Chemo-Mechanics from Atomistic Simulations

ERI：从原子模拟中学习化学力学本构方程

• 批准号: 2138431
• 负责人: Haoran Wang
• 金额: $ 20万
• 依托单位: Utah State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2138431&HistoricalAwards=false
• 关键词: ERI; Learning; Constitutive; Equations; Chemo

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).In this Engineering Research Initiation (ERI) project, the governing laws to describe the mechanics of materials in response to applied force is studied. These laws of mechanics are also known as constitutive relations. Traditionally, the constitutive relations are measured from experiments or based on conservation relations. With the development of materials science, new materials are emerging to achieve multiple functionalities. As a result, the constitutive relations are becoming more complicated. For example, materials need to work under electrical fields or chemical potentials. We need to know how materials respond to different external stimuli. The traditional ways of building constitutive relations would no longer work. This project is to develop a new way to build constitutive relations by learning from simulation data. Here, the constitutive relations for silicon anodes in lithium-ion batteries will be constructed. The study will help us better understand electrode materials and how they fail. The new approach for constitutive modeling can potentially uncover new knowledge in materials. It will be an important step towards the design of high-capacity rechargeable batteries. Therefore, results from this project will contribute to the development of the national economy and sustainability. This project will support undergraduate and graduate students, and particularly encourage underrepresented students to join. The supported students will gain research experience across mechanics, computations, data science, and energies. In this project, the sparse regression method will be used to learn the governing differential equations of chemo-mechanics for the silicon anodes in lithium-ion batteries. Sparse regression can identify the functional forms of the constitutive equations entirely based on spatiotemporal data which are obtained from Monte Carlo molecular dynamics simulations. This research includes three tasks: (i) learning the chemo-mechanics equations for bulk diffusion to compare with existing experiments and models for validation purposes; (ii) learning the chemo-mechanics equations for surface diffusion to gain new understandings of the coupling relations of surface diffusion and mechanics; (iii) learning the probabilistic chemo-mechanics equations to demonstrate the capabilities of the data-driven method in quantifying the uncertainties of material behavior.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.

该奖项的全部或部分资金来源于《2021 年美国救援计划法案》（公法 117-2）。在这个工程研究启动 (ERI) 项目中，研究了描述材料力学响应外力的控制定律。这些力学定律也称为本构关系。传统上，本构关系是通过实验或基于守恒关系来测量的。随着材料科学的发展，新材料不断涌现以实现多种功能。其结果是，本构关系变得更加复杂。例如，材料需要在电场或化学势下工作。我们需要知道材料如何响应不同的外部刺激。传统的构建本构关系的方式不再有效。该项目旨在开发一种通过学习模拟数据来构建本构关系的新方法。在这里，将构建锂离子电池中硅负极的本构关系。这项研究将帮助我们更好地了解电极材料及其失效原因。本构建模的新方法有可能揭示材料中的新知识。这将是朝着高容量可充电电池设计迈出的重要一步。因此，本项目的成果将有助于国民经济的发展和可持续发展。该项目将支持本科生和研究生，特别是鼓励代表性不足的学生加入。受支持的学生将获得力学、计算、数据科学和能源领域的研究经验。 在该项目中，稀疏回归方法将用于学习锂离子电池中硅阳极的化学力学控制微分方程。稀疏回归可以完全基于蒙特卡罗分子动力学模拟获得的时空数据来识别本构方程的函数形式。这项研究包括三个任务：（i）学习体扩散的化学力学方程，以与现有实验和模型进行比较以进行验证； (ii) 学习表面扩散的化学力学方程，以获得对表面扩散与力学耦合关系的新理解； (iii) 学习概率化学力学方程，以证明数据驱动方法在量化材料行为的不确定性方面的能力。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。