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/).

当今常用的几乎所有便携式电子设备（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，可以改善这些设备的功能。这项工作将产生新的计算方法，用于设计具有理想特性的电池，例如高功率输出和较长的寿命。新的计算方法将使用在胶片和游戏的计算机图形中成功描述复杂的体积结构（例如多孔岩石和海绵）的技术。这些计算机图形技术将应用于描述电池中的材料。新的计算方法没有专注于找到正确视觉外观的容积结构，而是专注于产生正确的性能特征（例如功率密度）的结构。新的体积描述将用于生成大量潜在的体积材料，这些模型将以电池性能和性能来表征。使用最近开发的机器学习技术，这种大量潜在模型将转换为一种方便在电池设计中使用的形式。除了提供工具来创建改进的便携式电池外，新的计算方法还可以进一步扩展并应用于涉及具有复杂体积结构的材料的其他问题，例如了解地质测量和设计文化遗产遗产纪念碑的设计保护策略和文物。但是，模拟需要数小时（在当前的多核系统上进行功率密度计算最多50小时），从而使迭代设计的前方模拟非常昂贵。如果有逆函数可以产生微观结构描述，则可以显着改进设计过程。创建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。要创建一个逆函数，我们需要一个微观结构描述，该描述比高分辨率网格的全枚举要低。程序模型可以提供如此较低的维度描述。该项目中探索的方法是为了找到适当的程序模型，这是基于在其他问题域中成功的结合和转换模型，以适应实际电池材料测量的数据。给定适当的程序模型，设计问题减少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。即使使用紧凑的微观结构描述，问题也太复杂了，无法在数学上倒转。使用机器学习（深度神经网络），而不是尝试找到数学功能。微观结构的数据库及其性能特征将通过从过程模型参数的大量采样中计算出的示例微观结构进行综合填充。这些样品将进行正向模拟，以计算属性（曲折度和面积密度）和性能特征（功率和能量密度）。然后将使用机器学习优化来找到模型参数和性能特征之间的关系，并且该关系将在设计过程中使用。查找程序模型以拟合数据然后学习从模型生成的合成数据的关系的总体方法将新的数据驱动方法的力量带入了电池设计领域。该项目产生的软件，数据和出版物将在项目网站（http://hpcg.purdue.edu/eager2018/）上找到。