Data Mining And Intermetallic Property Prediction
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Data-Mining and Intermetallic Property-Prediction
Author: David J. Fisher
language: en
Publisher: Materials Research Forum LLC
Release Date: 2022-08-05
Using a computer-aided data mining approach and available experimental data bases, the author discusses the prediction of the structures and properties of intermetallic alloy compounds. The book references 252 original resources with their direct web links for in-depth reading. Keywords: Data-Mining, Intermetallic Compounds, Structure-Mapping, Clustering Methods, Free Energy, Energy Landscapes of Compounds, Stable Groupings of Atoms, Intermetallic Phases, Crystal Unit Cell Size, Platonic Solids, Symmetries, Stoichiometries, Stability Fields.
Intermetallics
The fascinating world of intermetallics is largely unexplored. There are many exciting physical properties and important technological applications of intermetallics, from magnetism to superconductivity. The main focus of this book is on the statistics, topology and geometry of crystal structures and structure types of intermetallic phases. The underlying physics, in particular chemical bonding, is discussed whenever it helps understand the stability of structures and the origin of their physical properties. The authors' approach, based on the statistical analysis of more than twenty thousand intermetallic compounds in the data base Pearson's Crystal Data, uncovers important structural relationships and illustrates the relative simplicity of most of the general structural building principles. It also shows that a large variety of actual structures can be related to a rather small number of aristotypes. The text aims to be readable and beneficial in one way or another to everyone interested in intermetallic phases, from graduate students to experts in solid state chemistry and physics, and materials science. For that purpose it avoids the use of enigmatic abstract terminology for the classification of structures. Instead, it focuses on the statistical analysis of crystal structures and structure types in order to draw together a larger overview of intermetallics, and indicate the gaps in it - areas still to be explored, and potential sources of worthwhile research. The text should be read as a reference guide to the incredibly rich world of intermetallic phases.
Diagnostic Test Approaches to Machine Learning and Commonsense Reasoning Systems
The consideration of symbolic machine learning algorithms as an entire class will make it possible, in the future, to generate algorithms, with the aid of some parameters, depending on the initial users' requirements and the quality of solving targeted problems in domain applications. Diagnostic Test Approaches to Machine Learning and Commonsense Reasoning Systems surveys, analyzes, and compares the most effective algorithms for mining all kinds of logical rules. Global academics and professionals in related fields have come together to create this unique knowledge-sharing resources which will serve as a forum for future collaborations.