EAGER: Collaborative Research: Inverse Procedural Material Modeling for Battery Design

EAGER：协作研究：电池设计的逆过程材料建模

• 批准号: 1747522
• 负责人: Holly Rushmeier
• 金额: $ 10万
• 依托单位: Yale University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2018-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1747522&HistoricalAwards=false
• 关键词: EAGER; Collaborative; Research; Inverse; Procedural

Nearly all portable electronic devices commonly used today -- cameras, phones, music players and the like -- rely on rechargeable Lithium-ion batteries. Improvements in the capabilities of these devices can be achieved by improving the design of these batteries. This work will produce new computational methods for designing batteries with desirable properties such as high power output and long lifespan. The new computational methods will use techniques that have successfully described complex volumetric structures (such as porous rocks and sponges) in computer graphics for film and games. These computer graphics techniques will be applied to describing the materials in batteries. Instead of focusing on finding volumetric structures that give the correct visual appearance, the new computational methods will focus on structures that produce the correct performance characteristics such as power density. The new volumetric descriptions will be used to generate a large number of potential volumetric materials, and these models will be characterized in terms of battery properties and performance. Using recently developed machine learning techniques, this large number of potential models will be converted into a form that is convenient to use in battery design. In addition to providing tools to create improved portable batteries, the new computational methods have the potential to be further extended and applied to other problems involving materials with complex volumetric structure such as understanding geologic measurements and designing conservation strategies for cultural heritage monuments and artifacts.A straightforward approach to battery design is to theorize material microstructures, run forward simulations to assess their performance, and evaluate the results. However, simulations require hours (up to 50 hours on current multi-core systems for power density calculations), making forward simulation prohibitively expensive for iterative design. The design process can be dramatically improved if an inverse function is available that can produce a microstructure description given desired performance characteristics. Barriers to creating such an inverse function are the complexity of microstructure descriptions and the relationship between structure and performance. To create an inverse function, we need a microstructure description that is lower in dimension than a full enumeration of a high-resolution grid. A procedural model can provide such a lower dimensional description. The approach explored in this project for finding appropriate procedural models is based on combining and transforming models that have been successful in other problem domains to fit data from real battery material measurements. Given an appropriate procedural model, the design problem is reduced to determining the procedural model parameters that generate the input; a problem called "inverse procedural modeling". Even with a compact microstructure description, the problem is too complex to be mathematically inverted. Rather than attempt to find a mathematical function, machine learning (deep neural networks) are used. A database of microstructures and their performance characteristics will be populated synthetically with example microstructures computed from a large sampling of procedural model parameters. Forward simulations will be run on these samples to compute properties (tortuosity and area density) and performance characteristics (power and energy density.) Machine learning optimizations will then be used to find the relationship between model parameters and performance characteristics and this relationship will be used in the design process. The overall method of finding procedural models to fit data and then learning the relationships from synthetic data generated from the models brings the power of new data-driven approaches to the domain of battery design. The software, data and publications resulting from this project will be available at the project website (http://hpcg.purdue.edu/Eager2018/).

如今几乎所有常用的便携式电子设备--相机、电话、音乐播放器等--都依赖于可充电锂离子电池。通过改进这些电池的设计，可以实现这些设备的能力的提高。这项工作将产生新的计算方法，用于设计具有高功率输出和长寿命等理想特性的电池。新的计算方法将使用在电影和游戏的计算机图形中成功描述复杂体积结构（如多孔岩石和海绵）的技术。这些计算机图形技术将被应用于描述电池中的材料。新的计算方法将重点放在产生正确性能特征（如功率密度）的结构上，而不是集中在寻找提供正确视觉外观的体积结构上。新的体积描述将用于生成大量潜在的体积材料，这些模型将在电池特性和性能方面进行表征。使用最近开发的机器学习技术，这大量的潜在模型将被转换成便于在电池设计中使用的形式。除了提供制造改进的便携式电池的工具外，新的计算方法还具有进一步扩展和应用于涉及具有复杂体积结构的材料的其他问题的潜力，例如理解地质测量和设计文化遗产纪念碑和人工制品的保护策略。电池设计的直接方法是将材料微观结构理论化，运行正向模拟以评估其性能，并评估结果。然而，仿真需要数小时（在当前的多核系统上用于功率密度计算高达50小时），使得正向仿真对于迭代设计来说过于昂贵。设计过程可以显着改善，如果反函数是可用的，可以产生一个给定的性能特性的微观结构描述。建立这种反函数的障碍是微观结构描述的复杂性以及结构与性能之间的关系。为了创建一个反函数，我们需要一个比高分辨率网格的完整枚举在维度上更低的微观结构描述。过程模型可以提供这种较低维度的描述。在这个项目中探索的方法，寻找适当的程序模型是基于组合和转换模型，已成功地在其他问题领域，以适应数据从真实的电池材料测量。给定一个适当的程序模型，设计问题被简化为确定生成输入的程序模型参数;一个称为“逆程序建模”的问题。即使有一个紧凑的微观结构的描述，这个问题是太复杂了，数学反演。而不是试图找到一个数学函数，使用机器学习（深度神经网络）。微观结构及其性能特征的数据库将综合填充从程序模型参数的大样本计算的示例微观结构。将对这些样本进行正向模拟，以计算属性（弯曲度和面积密度）和性能特征（功率和能量密度）。然后，机器学习优化将用于找到模型参数和性能特征之间的关系，并且这种关系将用于设计过程。找到程序模型来拟合数据，然后从模型生成的合成数据中学习关系的整体方法，为电池设计领域带来了新的数据驱动方法的力量。该项目产生的软件、数据和出版物将在项目网站（http：//hpcg.purdue.edu/publicer2018/）上提供。