Information Science for Materials Discovery and Design, Artificial Intelligence for Materials Science, Materials Discovery and Design: By Means of Data Science and Optimal Learning, Springer マテリアルズ・インフォマティクス 特選3冊セット, 9783319238708, 9783030683092, 9783319994642

Springer マテリアルズ・インフォマティクス 特選3冊セット

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Springer マテリアルズ・インフォマティクス 特選3冊セット




Information Science for Materials Discovery and Design
Artificial Intelligence for Materials Science
Materials Discovery and Design: By Means of Data Science and Optimal Learning
Springer マテリアルズ・インフォマティクス 特選3冊セット
著者・編者 Lookman, T. et al.
Cheng, Y. et al.
Lookman, T. et al.
発行元 Springer
発行年/月 2015年12月
2021年 3月
装丁 Hardcover
ISBN 978-3-319-23870-8
発送予定 海外倉庫よりお取り寄せ 2-3週間以内に発送致します

Description (Information Science for Materials Discovery and Design)

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.



- A Perspective on Materials Informatics: State-of-the-Art and Challenges
- Information-Driven Experimental Design in Materials Science
- Bayesian Optimization for Materials Design
- Small-Sample Classification
- Data Visualization and Structure Identification
- Inference of Hidden Structures in Complex Physical Systems by Multi-scale Clustering
- On the Use of Data Mining Techniques to Build High-Density, Additively-Manufactured Parts
- Optimal Dopant Selection for Water Splitting with Cerium Oxides: Mining and Screening First Principles Data
- Toward Materials Discovery with First-Principles Datasets and Learning Methods
- Materials Informatics Using Ab initio Data: Application to MAX Phases
- Symmetry-Adapted Distortion Modes as Descriptors for Materials Informatics
- Discovering Electronic Signatures for Phase Stability of Intermetallics via Machine Learning
- Combinatorial Materials Science, and a Perspective on Challenges in Data Acquisition, Analysis and Presentation
- High Throughput Combinatorial Experimentation + Informatics = Combinatorial Science


Description (Artificial Intelligence for Materials Science)

Machine learning methods have lowered the cost of exploring new structures of unknown compounds, and can be used to predict reasonable expectations and subsequently validated by experimental results. As new insights and several elaborative tools have been developed for materials science and engineering in recent years, it is an appropriate time to present a book covering recent progress in this field.

Searchable and interactive databases can promote research on emerging materials. Recently, databases containing a large number of high-quality materials properties for new advanced materials discovery have been developed. These approaches are set to make a significant impact on human life and, with numerous commercial developments emerging, will become a major academic topic in the coming years.

This authoritative and comprehensive book will be of interest to both existing researchers in this field as well as others in the materials science community who wish to take advantage of these powerful techniques. The book offers a global spread of authors, from USA, Canada, UK, Japan, France, Russia, China and Singapore, who are all world recognized experts in their separate areas. With content relevant to both academic and commercial points of view, and offering an accessible overview of recent progress and potential future directions, the book will interest graduate students, postgraduate researchers, and consultants and industrial engineers.



- Brief Introduction of the Machine Learning Method
- Machine Learning for High-Entropy Alloys
- Two-Way TrumpetNets and TubeNets for Identification of Material Parameters
- Machine Learning Interatomic Force Fields for Carbon Allotropic Materials
- Genetic Algorithms
- Accelerated Discovery of Thermoelectric Materials Using Machine Learning
- Thermal Nanostructure Design by Materials Informatics
- Machine Learning Accelerated Insights of Perovskite Materials


Description (Materials Discovery and Design: By Means of Data Science and Optimal Learning)

This book addresses the current status, challenges and future directions of data-driven materials discovery and design. It presents the analysis and learning from data as a key theme in many science and cyber related applications. The challenging open questions as well as future directions in the application of data science to materials problems are sketched. Computational and experimental facilities today generate vast amounts of data at an unprecedented rate. The book gives guidance to discover new knowledge that enables materials innovation to address grand challenges in energy, environment and security, the clearer link needed between the data from these facilities and the theory and underlying science. The role of inference and optimization methods in distilling the data and constraining predictions using insights and results from theory is key to achieving the desired goals of real time analysis and feedback. Thus, the importance of this book lies in emphasizing that the full value of knowledge driven discovery using data can only be realized by integrating statistical and information sciences with materials science, which is increasingly dependent on high throughput and large scale computational and experimental data gathering efforts. This is especially the case as we enter a new era of big data in materials science with the planning of future experimental facilities such as the Linac Coherent Light Source at Stanford (LCLS-II), the European X-ray Free Electron Laser (EXFEL) and MaRIE (Matter Radiation in Extremes), the signature concept facility from Los Alamos National Laboratory. These facilities are expected to generate hundreds of terabytes to several petabytes of in situ spatially and temporally resolved data per sample. The questions that then arise include how we can learn from the data to accelerate the processing and analysis of reconstructed microstructure, rapidly map spatially resolved properties from high throughput data, devise diagnostics for pattern detection, and guide experiments towards desired targeted properties. The authors are an interdisciplinary group of leading experts who bring the excitement of the nascent and rapidly emerging field of materials informatics to the reader.



- Dimensions, Bits, and Wows in Accelerating Materials Discovery
- Is Automated Materials Design and Discovery Possible?
- Importance of Feature Selection in Machine Learning and Adaptive Design for Materials
- Bayesian Approaches to Uncertainty Quantification and Structure Refinement from X-Ray Diffraction
- Deep Data Analytics in Structural and Functional Imaging of Nanoscale Materials
- Data Challenges of In Situ X-Ray Tomography for Materials Discovery and Characterization
- Overview of High-Energy X-Ray Diffraction Microscopy (HEDM) for Mesoscale Material Characterization in Three-Dimensions
- Bragg Coherent Diffraction Imaging Techniques at 3rd and 4th Generation Light Sources
- Automatic Tuning and Control for Advanced Light Sources