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

Research output: Chapter in Book/Report/Conference proceedingChapter


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.

Original languageEnglish
Title of host publicationEnergy Storage and Conversion Materials
Subtitle of host publicationProperties, Methods, and Applications
PublisherCRC Press
Number of pages17
ISBN (Electronic)9781000868722
ISBN (Print)9781032434216
Publication statusPublished - 2023 Jan 1

All Science Journal Classification (ASJC) codes

  • General Engineering
  • General Chemical Engineering
  • General Environmental Science
  • General Materials Science


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