The triennial International Alloy Conferences (lACs) aim at the identification and promotion of the common elements developed in the study, either experimental, phenomenological, or theoretical and computational, of materials properties across materials types, from metals to minerals. To accomplish this goal, the lACs bring together scientists from a wide spectrum of materials science including experiment, theory, modeling, and computation, incorporating a broad range of materials properties. The first lAC, lAC-I, took place in Athens, Greece, June 16-21, 1996. The present volume of proceedings contains the papers presented at IAC-2, that took place in Davos, Switzerland, August 8-13, 1999. The topics in this book fall into several themes, which suggest a number of different classification schemes. We have chosen a scheme that classifies the papers in the volume into the categories Microstructural Properties; Ordering, Kinetics and Diffusion; Magnetic Properties and Elastic Properties. We have juxtaposed apparently disparate of revealing the dynamic character approaches to similar physical processes, in the hope of the processes under consideration. We hope this will invigorate new kinds of discussion and reveal challenges and new avenues to the description and prediction of properties of materials in the solid state and the conditions that produce them.

One of the key aspects of this volume is to cut across the traditional taxonomy of disciplines in the study of alloys. Hence there has been a deliberate attempt to integrate the different approaches taken towards alloys as a class of materials in different fields, ranging from geology to metallurgical engineering. The emphasis of this book is to highlight commonalities between different fields with respect to how alloys are studied. The topics in this book fall into several themes, which suggest a number of different classification schemes. We have chosen a scheme that classifies the papers in the volume into the categories Microstructural Considerations, Ordering, Kinetics and Diffusion, Magnetic Considerations and Elastic Considerations. The book has juxtaposed apparently disparate approaches to similar physical processes, in the hope of revealing a more dynamic character of the processes under consideration. This monograph will invigorate new kinds of discussion and reveal challenges and new avenues to the description and prediction of properties of materials in the solid state and the conditions that produce them.

This book deals with an information-driven approach to plan materials discovery and design, iterative learning. The authors present contrasting but complementary approaches, such as those based on high throughput calculations, combinatorial experiments or data driven discovery, together with machine-learning methods. Similarly, statistical methods successfully applied in other fields, such as biosciences, are presented. The content spans from materials science to information science to reflect the cross-disciplinary nature of the field. A perspective is presented that offers a paradigm (codesign loop for materials design) to involve iteratively learning from experiments and calculations to develop materials with optimum properties. Such a loop requires the elements of incorporating domain materials knowledge, a database of descriptors (the genes), a surrogate or statistical model developed to predict a given property with uncertainties, performing adaptive experimental design to guide the next experiment or calculation and aspects of high throughput calculations as well as experiments. The book is about manufacturing with the aim to halving the time to discover and design new materials. Accelerating discovery relies on using large databases, computation, and mathematics in the material sciences in a manner similar to the way used to in the Human Genome Initiative. Novel approaches are therefore called to explore the enormous phase space presented by complex materials and processes. To achieve the desired performance gains, a predictive capability is needed to guide experiments and computations in the most fruitful directions by reducing not successful trials. Despite advances in computation and experimental techniques, generating vast arrays of data; without a clear way of linkage to models, the full value of data driven discovery cannot be realized. Hence, along with experimental, theoretical and computational materials science, we need to add a "fourth leg'' to our toolkit to make the "Materials Genome'' a reality, the science of Materials Informatics.

Materials informatics: a hot topic area in materials science, aims to combine traditionally bio-led informatics with computational methodologies, supporting more efficient research by identifying strategies for time- and cost-effective analysis.

The discovery and maturation of new materials has been outpaced by the thicket of data created by new combinatorial and high throughput analytical techniques. The elaboration of this "quantitative avalanche"-and the resulting complex, multi-factor analyses required to understand it-means that interest, investment, and research are revisiting informatics approaches as a solution.

This work, from Krishna Rajan, the leading expert of the informatics approach to materials, seeks to break down the barriers between data management, quality standards, data mining, exchange, and storage and analysis, as a means of accelerating scientific research in materials science.

This solutions-based reference synthesizes foundational physical, statistical, and mathematical content with emerging experimental and real-world applications, for interdisciplinary researchers and those new to the field.
Identifies and analyzes interdisciplinary strategies (including combinatorial and high throughput approaches) that accelerate materials development cycle times and reduces associated costs

Mathematical and computational analysis aids formulation of new structure-property correlations among large, heterogeneous, and distributed data sets

Practical examples, computational tools, and software analysis benefits rapid identification of critical data and analysis of theoretical needs for future problems "

One of the key aspects of this volume is to cut across the traditional taxonomy of disciplines in the study of alloys. Hence there has been a deliberate attempt to integrate the different approaches taken towards alloys as a class of materials in different fields, ranging from geology to metallurgical engineering. The emphasis of this book is to highlight commonalities between different fields with respect to how alloys are studied. The topics in this book fall into several themes, which suggest a number of different classification schemes. We have chosen a scheme that classifies the papers in the volume into the categories Microstructural Considerations, Ordering, Kinetics and Diffusion, Magnetic Considerations and Elastic Considerations. The book has juxtaposed apparently disparate approaches to similar physical processes, in the hope of revealing a more dynamic character of the processes under consideration. This monograph will invigorate new kinds of discussion and reveal challenges and new avenues to the description and prediction of properties of materials in the solid state and the conditions that produce them.

The triennial International Alloy Conferences (lACs) aim at the identification and promotion of the common elements developed in the study, either experimental, phenomenological, or theoretical and computational, of materials properties across materials types, from metals to minerals. To accomplish this goal, the lACs bring together scientists from a wide spectrum of materials science including experiment, theory, modeling, and computation, incorporating a broad range of materials properties. The first lAC, lAC-I, took place in Athens, Greece, June 16-21, 1996. The present volume of proceedings contains the papers presented at IAC-2, that took place in Davos, Switzerland, August 8-13, 1999. The topics in this book fall into several themes, which suggest a number of different classification schemes. We have chosen a scheme that classifies the papers in the volume into the categories Microstructural Properties; Ordering, Kinetics and Diffusion; Magnetic Properties and Elastic Properties. We have juxtaposed apparently disparate of revealing the dynamic character approaches to similar physical processes, in the hope of the processes under consideration. We hope this will invigorate new kinds of discussion and reveal challenges and new avenues to the description and prediction of properties of materials in the solid state and the conditions that produce them.

This book deals with an information-driven approach to plan materials discovery and design, iterative learning. The authors present contrasting but complementary approaches, such as those based on high throughput calculations, combinatorial experiments or data driven discovery, together with machine-learning methods. Similarly, statistical methods successfully applied in other fields, such as biosciences, are presented. The content spans from materials science to information science to reflect the cross-disciplinary nature of the field. A perspective is presented that offers a paradigm (codesign loop for materials design) to involve iteratively learning from experiments and calculations to develop materials with optimum properties. Such a loop requires the elements of incorporating domain materials knowledge, a database of descriptors (the genes), a surrogate or statistical model developed to predict a given property with uncertainties, performing adaptive experimental design to guide the next experiment or calculation and aspects of high throughput calculations as well as experiments. The book is about manufacturing with the aim to halving the time to discover and design new materials. Accelerating discovery relies on using large databases, computation, and mathematics in the material sciences in a manner similar to the way used to in the Human Genome Initiative. Novel approaches are therefore called to explore the enormous phase space presented by complex materials and processes. To achieve the desired performance gains, a predictive capability is needed to guide experiments and computations in the most fruitful directions by reducing not successful trials. Despite advances in computation and experimental techniques, generating vast arrays of data; without a clear way of linkage to models, the full value of data driven discovery cannot be realized. Hence, along with experimental, theoretical and computational materials science, we need to add a "fourth leg'' to our toolkit to make the "Materials Genome'' a reality, the science of Materials Informatics.