The development of new battery materials can be a time-consuming and expenise endeavor. However, artificial intelligence may hold the key to more rapid and cost-effective material development. To achieve high battery efficiency and operational reliability, predictive intelligence, and data analytics will play key roles as artificial intelligence becomes more disruptive in the battery technology space. Long and costly material development cycles are being replaced by quick and afforable computational proceessing methods. This meeting will discuss how machine learning can be used to create and select battery materials and how digital twins can be used as a tool to monitor battery degredation. Coverage will include, but is not limited to: • Data Processing and Selection Methods • Advanaced Material Modelling Techniques • Big Data and Deep Learning • Using Neural Networks for SOC & SOH • Using Digital Twins to Monitor Materials Degredation