| Abstract Scope |
Battery design and discovery has traditionally followed a trial and error process. To accelerate this process, engineers and scientists have formalized physical descriptions of batteries into computational models. The U.S. Department of Energy’s Computer Aided Engineering of Batteries program, for example, supported software companies to scale well-known transport/reaction models at the Li-ion battery electrode length scale to solve large 3D battery design problems around packaging, heat and electron transport, performance, safety and crashworthiness of electric vehicles. Present research looks to deepen our knowledge at the sub-electrode or microstructure length scale to better understand chemo/mechanical interactions that govern degradation of today’s graphite/nickelate chemistries and operating performance of future Li/silicon/sulfur chemistries. Multi-scale high performance computing models and machine learning support this research. Machine learning algorithms interpret microscopy and electrochemical data to provide better descriptions of material mechanical evolution. Machine learning also identifies chemical side-reaction sequences from atomistic and molecular calculations. |