Humans have been “manually” extracting patterns from data for centuries, but the increasing volume of data in modern times has called for more automatic approaches. Early methods of identifying patterns in data include Bayes’ theorem (1700s) and Regression analysis (1800s). The proliferation, ubiquity and incre- ing power of computer technology has increased data collection and storage. As data sets have grown in size and complexity, direct hands-on data analysis has - creasingly been augmented with indirect, automatic data processing. Data mining has been developed as the tool for extracting hidden patterns from data, by using computing power and applying new techniques and methodologies for knowledge discovery. This has been aided by other discoveries in computer science, such as Neural networks, Clustering, Genetic algorithms (1950s), Decision trees (1960s) and Support vector machines (1980s). Data mining commonlyinvolves four classes of tasks: • Classi cation: Arranges the data into prede ned groups. For example, an e-mail program might attempt to classify an e-mail as legitimate or spam. Common algorithmsinclude Nearest neighbor,Naive Bayes classi er and Neural network. • Clustering: Is like classi cation but the groups are not prede ned, so the algorithm will try to group similar items together. • Regression: Attempts to nd a function which models the data with the least error. A common method is to use Genetic Programming. • Association rule learning: Searches for relationships between variables. For example, a supermarket might gather data of what each customer buys.

Humans have been “manually” extracting patterns from data for centuries, but the increasing volume of data in modern times has called for more automatic approaches. Early methods of identifying patterns in data include Bayes’ theorem (1700s) and Regression analysis (1800s). The proliferation, ubiquity and incre- ing power of computer technology has increased data collection and storage. As data sets have grown in size and complexity, direct hands-on data analysis has - creasingly been augmented with indirect, automatic data processing. Data mining has been developed as the tool for extracting hidden patterns from data, by using computing power and applying new techniques and methodologies for knowledge discovery. This has been aided by other discoveries in computer science, such as Neural networks, Clustering, Genetic algorithms (1950s), Decision trees (1960s) and Support vector machines (1980s). Data mining commonlyinvolves four classes of tasks: • Classi cation: Arranges the data into prede ned groups. For example, an e-mail program might attempt to classify an e-mail as legitimate or spam. Common algorithmsinclude Nearest neighbor,Naive Bayes classi er and Neural network. • Clustering: Is like classi cation but the groups are not prede ned, so the algorithm will try to group similar items together. • Regression: Attempts to nd a function which models the data with the least error. A common method is to use Genetic Programming. • Association rule learning: Searches for relationships between variables. For example, a supermarket might gather data of what each customer buys.

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.

Zeitschrift für Kristallographie. Supplement Volume 36 presents the complete Abstracts of all contributions to the 24th Annual Conference of the German Crystallographic Society in Stuttgart (Germany) 2016: - Plenary Talks - Microsymposia - Poster Session Supplement Series of Zeitschrift für Kristallographie publishes Abstracts of international conferences on the interdisciplinary field of crystallography.

Zeitschrift für Kristallographie. Supplement Volume 41 presents the complete Abstracts of all contributions to the 29th Annual Conference of the German Crystallographic Society in Hamburg (Germany) 2021: - Plenary Talks - Microsymposia - Poster Session Supplement Series of Zeitschrift für Kristallographie publishes Abstracts of international conferences on the interdisciplinary field of crystallography.

Zeitschrift für Kristallographie. Supplement Volume 35 presents the complete Abstracts of all contributions to the 23rd Annual Conference of the German Crystallographic Society in Göttingen (Germany) 2015: - Plenary Talks - Microsymposia - Poster Session Supplement Series of Zeitschrift für Kristallographie publishes Abstracts of international conferences on the interdisciplinary field of crystallography.

Zeitschrift für Kristallographie. Supplement Volume 39 presents the complete Abstracts of all contributions to the 27th Annual Conference of the German Crystallographic Society in Leipzig (Germany) 2019: - Plenary Talks - Microsymposia - Poster Session Supplement Series of Zeitschrift für Kristallographie publishes Abstracts of international conferences on the interdisciplinary field of crystallography.

This volume summarises recent developments and highlights new techniques which will define possible future directions for small molecule X-ray crystallography. It provides an insight into how specific aspects of crystallography are developing and shows how they may interact or integrate with other areas of science. The development of more sophisticated equipment and the massive rise in computing power has made it possible to solve the three-dimensional structure of an organic molecule within hours if not minutes. This successful trajectory has resulted in the ability to study ever more complex molecules and use smaller and smaller crystals. The structural parameters for over a million organic and organometallic compounds are now archived in the most commonly used database and this wealth of information creates a new set of problems for future generations of scientists. The volume provides some insight into how users of crystallographic structural data banks can navigate their way through a world where “big data” has become the norm. The coupling of crystallography to quantum chemical calculations provides detailed information about electron distributions in crystals affording a much more detailed analysis of bonding than has been possible previously. In quantum crystallography, quantum mechanical wavefunctions are used to extract information about bonding and properties from the measured X-ray structure factors. The advent of quantum crystallography has resulted in form and structure factors derived from quantum mechanics which have been used in advanced refinement and wavefunction fitting. This volume describes how quantum mechanically derived atomic form factors and structure factors are constructed to allow the improved description of the diffraction experiment. It further discusses recent developments in this field and illustrates their applications with a wide range of examples. This volume will be of interest to chemists and crystallographers with an interest in the synthesis, characterisation and physical and catalytic properties of solid-state materials. It will also be relevant for the community of computational chemists who study chemical systems. Postgraduate students entering the field will benefit from a historical introduction to the way in which scientists have used the data derived from crystallography to develop new structural and bonding models.

This book constitutes the refereed proceedings of the 12th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2016, held in New York, NY, USA in July 2016. The 58 regular papers presented in this book were carefully reviewed and selected from 169 submissions. The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data mining methods for the different multimedia data types such as image mining, text mining, video mining and Web mining.