Offers advantages and limitations of several AI concepts and their proper implementation in various data types generated through experiments and computer simulations and from industries in different file formats
Helps readers develop predictive models through AI/ML algorithms by writing their own computer code or using resources where they do not have to write code
Provides downloadable resources such as MATLAB GUI/APP and Python implementation that can be used on common mobile devices
Discusses the CALPHAD approach and ways to use data generated from it
Features a chapter on metallurgical/materials concepts to help readers understand the case studies and thus proper implementation of AI/ML algorithms under the framework of data-driven materials science
This book is written for materials scientists and metallurgists interested in the application of AI, ML, and data science in the development of new materials.