Materials Data Science

This text covers all of the data science, machine learning, and deep learning topics relevant to materials science and engineering, accompanied by numerous examples and applications. Almost all methods and algorithms introduced are implemented from scratch using Python and NumPy.

The book starts with an introduction to statistics and probabilities, explaining important concepts such as random variables and probability distributions, Bayes' theorem and correlations, sampling techniques, and exploratory data analysis, and puts them in the context of materials science and engineering. Therefore, it serves as a valuable primer for both undergraduate and graduate students, as well as a review for research scientists and practicing engineers.

The second part provides an in-depth introduction of (statistical) machine learning. It begins with outlining fundamental concepts and proceeds to explore a variety of supervised learning techniques for regression and classification, including advanced methods such as kernel regression and support vector machines. The section on unsupervised learning emphasizes principal component analysis, and also covers manifold learning (t-SNE and UMAP) and clustering techniques. Additionally, feature engineering, feature importance, and cross-validation are introduced.

The final part on neural networks and deep learning aims to promote an understanding of these methods and dispel misconceptions that they are a black box. The complexity gradually increases until fully connected networks can be implemented. Advanced techniques and network architectures, including GANs, are implemented from scratch using Python and NumPy, which facilitates a comprehensive understanding of all the details and enables the user to conduct their own experiments in Deep Learning.

Introduces machine learning/deep learning methods in detail based on examples and data from materials science Covers all theoretical foundations in an accessible manner, tailored to materials scientists and engineers Maximizes intuitive understanding with materials science and physics examples, coding exercises, and online material

Autorentext

Prof. Dr. Stefan Sandfeld is Director of the Institute for Advanced Simulation: Materials Data Science and Informatics (IAS-9) Forschungszentrum Juelich, Germany; and Professor/Chair of Materials Data Science and Materials Informatics, RWTH Aachen University.

Inhalt
Introduction to Data, Data Mining, and Prediction in Materials Science.- A Primer on Probabilities, Distributions and Statistics.- Introduction to Statistical Machine Learning.- Artificial Neural Networks and Deep Learning.- Advanced Topics of Machine Learning and Data Mining in Materials Science and Engineering.- Conclusion Outlook.- Appendix.