Srikanth Allu, Oak Ridge National Laboratory
Scott Roberts, Sandia National Laboratories
John Turner, Oak Ridge National Laboratory
Increased adaptation of batteries by consumers, especially as energy storage devices for consumer electronics and electric vehicles has led the research towards improving our understanding of energetic material performance. As the supercomputers are becoming more powerful, scientific computing for physical phenomenon spanning multiple spatial and temporal scales will become realizable. Also, scientific experiments to characterize and quantify the transport and kinetic behavior at atomic and microstructure scale of energy storage materials are generating large volumes of data. In this symposium, we invite submissions that emphasize on numerical methods and simulation that uses machine learning algorithms based on data for improved computational efficiency and quantification of uncertainty that enable coupling to multiscale phenomenon.
