Introductory to Machine Learning Method and Its Applications in Li-Ion Batteries

The readily available large databases through the ubiquity of the internet and tremendous improvement in computing capability from the past decades such as the generalization of dedicated graphics cards and the Compute Unified Device Architecture (CUDA) parallel computing platform have provided a great foundation for the popularization of machine learning (ML) as it speeds up data processing of ML models. A score is granted when the behavior of the model leads to the desired result or the behavior itself is desired and deducted from the undesired one. Data can be self-generated by conducting experiments and performing high-throughput computation via various software, or from open-source databases that are available throughout the internet. Feature engineering is the process of extracting the most appropriate numerical values that provide important information relating to the goal of the ML model, and at the same time distinguishes between different materials from a given data.