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

Yale University, New Haven CT

Investigators

Abstract

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/).