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 formatsHelps readers to develop predictive models through AI/ML algorithms by writing their own computer code or using resources where they do not have to write codeCoversdownloadable resources such as MATLAB GUI/APP and Python implementation that can be used on common mobile devicesDiscusses the CALPHAD approach and ways to use data generated from itFeatures 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 scienceUses case studies to examine the importance of using unsupervised machine learning algorithms in determining patterns in datasets
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.