Methods, Tools, and Applications
1. Edition October 2019
304 Pages, Hardcover
1 Pictures (1 Colored Figures)
Provides everything readers need to know for applying the power of informatics to materials science
There is a tremendous interest in materials informatics and application of data mining to materials science. This book is a one-stop guide to the latest advances in these emerging fields. Bridging the gap between materials science and informatics, it introduces readers to up-to-date data mining and machine learning methods. It also provides an overview of state-of-the-art software and tools. Case studies illustrate the power of materials informatics in guiding the experimental discovery of new materials.
Materials Informatics: Methods, Tools and Applications is presented in two parts?Methodological Aspects of Materials Informatics and Practical Aspects and Applications. The first part focuses on developments in software, databases, and high-throughput computational activities. Chapter topics include open quantum materials databases; the ICSD database; open crystallography databases; and more. The second addresses the latest developments in data mining and machine learning for materials science. Its chapters cover genetic algorithms and crystal structure prediction; MQSPR modeling in materials informatics; prediction of materials properties; amongst others.
-Bridges the gap between materials science and informatics
-Covers all the known methodologies and applications of materials informatics
-Presents case studies that illustrate the power of materials informatics in guiding the experimental quest for new materials
-Examines the state-of-the-art software and tools being used today
Materials Informatics: Methods, Tools and Applications is a must-have resource for materials scientists, chemists, and engineers interested in the methods of materials informatics.
The Inorganic Crystal Structure Database: A tool for material sciences
PAULING FILE - Towards a holistic view
From topological descriptors to expert systems: a route to predictable materials
A high-throughput computational study driven by the AiiDA materials informatics framework and the PAULING FILE as reference database
Modeling materials quantum properties with machine learning
Automated computation of materials properties
Cognitive Chemistry? The marriage of machine learning and chemistry to accelerate materials discovery
Machine learning interatomic potentials for global optimization and molecular dynamics simulation
Alexander Tropsha, PhD, is K.H. Lee Distinguished Professor and Associate Dean for Pharmacoinformatics and Data Science at the UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill.
Stefano Curtarolo, PhD, is Professor in Materials Science, Electrical Engineering, and Physics and Director of the Center for Materials Genomics at Duke University, North Carolina.