Homology and Machine Learning for Materials Informatics PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Homology and Machine Learning for Materials Informatics PDF full book. Access full book title Homology and Machine Learning for Materials Informatics by Bart Olsthoorn. Download full books in PDF and EPUB format.
Author: Bart Olsthoorn
Publisher:
ISBN: 9789180405058
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
Author: Bart Olsthoorn
Publisher:
ISBN: 9789180405058
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
Author: Tongyi Zhang
Publisher: Springer
ISBN: 9789819979912
Category : Technology & Engineering
Languages : en
Pages : 0
Get Book Here
Book Description
This textbook educates current and future materials workers, engineers, and researchers on Materials Informatics. Volume I serves as an introduction, merging AI, ML, materials science, and engineering. It covers essential topics and algorithms in 11 chapters, including Linear Regression, Neural Networks, and more. Suitable for diverse fields like materials science, physics, and chemistry, it enables quick and easy learning of Materials Informatics for readers without prior AI and ML knowledge.
Author: Turab Lookman
Publisher: Springer
ISBN: 331923871X
Category : Technology & Engineering
Languages : en
Pages : 316
Get Book Here
Book Description
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.
Author: Turab Lookman
Publisher: Springer
ISBN: 3319994654
Category : Science
Languages : en
Pages : 266
Get Book Here
Book Description
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.
Author: Isao Tanaka
Publisher: Springer
ISBN: 9811076170
Category : Technology & Engineering
Languages : en
Pages : 296
Get Book Here
Book Description
This open access book brings out the state of the art on how informatics-based tools are used and expected to be used in nanomaterials research. There has been great progress in the area in which “big-data” generated by experiments or computations are fully utilized to accelerate discovery of new materials, key factors, and design rules. Data-intensive approaches play indispensable roles in advanced materials characterization. "Materials informatics" is the central paradigm in the new trend. "Nanoinformatics" is its essential subset, which focuses on nanostructures of materials such as surfaces, interfaces, dopants, and point defects, playing a critical role in determining materials properties. There have been significant advances in experimental and computational techniques to characterize individual atoms in nanostructures and to gain quantitative information. The collaboration of researchers in materials science and information science is growing actively and is creating a new trend in materials science and engineering.
Author: Keisuke Takahashi
Publisher: Springer Nature
ISBN: 9819702178
Category :
Languages : en
Pages : 301
Get Book Here
Book Description
Author: Surya R. Kalidindi
Publisher: Elsevier
ISBN: 012410455X
Category : Technology & Engineering
Languages : en
Pages : 230
Get Book Here
Book Description
Custom design, manufacture, and deployment of new high performance materials for advanced technologies is critically dependent on the availability of invertible, high fidelity, structure-property-processing (SPP) linkages. Establishing these linkages presents a major challenge because of the need to cover unimaginably large dimensional spaces. Hierarchical Materials Informatics addresses objective, computationally efficient, mining of large ensembles of experimental and modeling datasets to extract this core materials knowledge. Furthermore, it aims to organize and present this high value knowledge in highly accessible forms to end users engaged in product design and design for manufacturing efforts. As such, this emerging field has a pivotal role in realizing the goals outlined in current strategic national initiatives such as the Materials Genome Initiative (MGI) and the Advanced Manufacturing Partnership (AMP). This book presents the foundational elements of this new discipline as it relates to the design, development, and deployment of hierarchical materials critical to advanced technologies. Addresses a critical gap in new materials research and development by presenting a rigorous statistical framework for the quantification of microstructure Contains several case studies illustrating the use of modern data analytic tools on microstructure datasets (both experimental and modeling)
Author: Phil De Luna
Publisher: Walter de Gruyter GmbH & Co KG
ISBN: 3110733250
Category : Computers
Languages : en
Pages : 235
Get Book Here
Book Description
Typical timelines to go from discovery to impact in the advanced materials sector are between 10 to 30 years. Advances in robotics and artificial intelligence are poised to accelerate the discovery and development of new materials dramatically. This book is a primer for any materials scientist looking to future-proof their careers and get ahead of the disruption that artificial intelligence and robotic automation is just starting to unleash. It is meant to be an overview of how we can use these disruptive technologies to augment and supercharge our abilities to discover new materials that will solve world’s biggest challenges. Written by world leading experts on accelerated materials discovery from academia (UC Berkeley, Caltech, UBC, Cornell, etc.), industry (Toyota Research Institute, Citrine Informatics) and national labs (National Research Council of Canada, Lawrence Berkeley National Labs).
Author: 张统一
Publisher:
ISBN: 9787030728982
Category : Machine learning
Languages : en
Pages : 0
Get Book Here
Book Description
Author: Koichi Hayashi
Publisher: Springer Nature
ISBN: 9819952352
Category : Technology & Engineering
Languages : en
Pages : 458
Get Book Here
Book Description
This book introduces characterizations of hyperordered structures using latest quantum beam technologies, the advanced theoretical methods for understanding the roles of the structures, and the state-of-the-arts materials containing the structures. In this book, the authors focus on the importance of defect complexes to improve functionality of crystals and that of orders of network structures to improve functionality of glass materials. These features can be regarded as interphases between perfect crystals and perfect amorphous, and they are the key factor for the evolution of materials science to a new dimension. The authors call such interphases "hyperordered structures" in this book. This is the first book that comprehensively summarizes glass science, defect science, and quantum beam science. It is valuable not only for active researchers in industry and academia but also graduate students.
