  The discoveries in this paper show that Intelligence Processing Units (IPUs)
offer a viable accelerator alternative to GPUs for machine learning (ML)
applications within the fields of materials science and battery research. We
investigate the process of migrating a model from GPU to IPU and explore
several optimization techniques, including pipelining and gradient
accumulation, aimed at enhancing the performance of IPU-based models.
Furthermore, we have effectively migrated a specialized model to the IPU
platform. This model is employed for predicting effective conductivity, a
parameter crucial in ion transport processes, which govern the performance of
multiple charge and discharge cycles of batteries. The model utilizes a
Convolutional Neural Network (CNN) architecture to perform prediction tasks for
effective conductivity. The performance of this model on the IPU is found to be
comparable to its execution on GPUs. We also analyze the utilization and
performance of Graphcore's Bow IPU. Through benchmark tests, we observe
significantly improved performance with the Bow IPU when compared to its
predecessor, the Colossus IPU.
