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Full Description
Focusing on the fundamentals of machine learning, this book covers broad areas of data-driven modeling, ranging from simple regression to advanced machine learning and optimization methods for applications in materials modeling and discovery. The book explains complex mathematical concepts in a lucid manner to ensure that readers from different materials domains are able to use these techniques successfully. A unique feature of this book is its hands-on aspect—each method presented herein is accompanied by a code that implements the method in open-source platforms such as Python. This book is thus aimed at graduate students, researchers, and engineers to enable the use of data-driven methods for understanding and accelerating the discovery of novel materials.
Contents
Part I: Introduction.- Part II: Basics of Machine Learning Methods.- Introduction to Data-Based Modeling.- Model Development.- Introduction to Machine Learning.- Quick Dive into Probabilistic Methods.- Optimization.- Part III: Application in Glass Science.- Property Prediction.- Glass Discovery.- Understanding Glass Physics.- Atomistic Modeling.- Future Directions.
