Lookman turab (74 risultati)

XIII, 276 p. Hardcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Springer Series in Materials Science, 275. Sprache: Englisch. …

Hardcover. Condizione: Good. New set of 2 books - sealed. Plastic covering has some open tears.

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Wraps. Condizione: Very good. Presumed First Edition, First printing. ii, 33, [1] pages. Color illustration. Footnotes. Dr. McCleskey is associated with the Los Alamos National Laboratory's Chemistry Division. The Chemistry Division is composed of the division office and five technical groups. Each group name gives an indication of the types of activities being pursued within the group. Including technical staff members, technicians, support personnel, students, and postdoctoral researchers, there are approximately 450 people in the division. Many of the research and development projects in the division span several groups and divisions, and a number of them involve partnerships with academia, industry, or both. Its annual budget of approximately $95 million comes from a diverse set of program offices within the Laboratory. These offices focus on nuclear weapons, threat reduction, homeland security, science, applied energy, and environment. They serve as the interfaces with program elements of the Department of Energy and other federal agencies, such as the Department of Defense, Department of Homeland Security, the National Institutes of Health, the National Science Foundation, and more. Postdoctoral and student employees account for roughly 20% of our workforce and join us from colleges and universities around the country and the world. They bring new ideas and techniques from their most recent appointments and contribute to our division's vitality and development. Over the years, many of our postdoctoral fellows have joined the Laboratory as technical staff members. Most advances in materials have focused on an observation approach that involves detailed characterization of a new material followed by integration of the material into applications based on the material properties. These properties are further tuned by changes in the processing conditions via a trial and error effort. The notion of predicting the properties of a material and systematically controlling the composition, defects and interfaces is typically viewed as something far off in the future. One exception that stands out is in the computer industry where the systematic characterization, and controlled manipulation of both purity and interfaces at ever increasingly finer resolution has lead to material advances and performance capabilities that not long ago would have been viewed by most people as impossible.…

hardcover. Condizione: Sehr gut. 289 Seiten; 9783319969138.2 Gewicht in Gramm: 1.

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Condizione: New.

Condizione: New. 1st edition NO-PA16APR2015-KAP.

Condizione: Very Good. Text block firm and clean, binding unblemished, boards straight, no highlights or underlining. Well packaged and promptly shipped from California. Partnered with Friends of the Library since 2010.

Condizione: New.

Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book provides a comprehensive introduction to ferroics and frustrated materials. Ferroics comprise a range of materials classes with functionalities such as magnetism, polarization, and orbital degrees of freedom and strain. Frustration, due to geometrical constraints, and disorder, due to chemical and/or structural inhomogeneities, can lead to glassy behavior, which has either been directly observed or inferred in a range of materials classes from model systems such as artificial spin ice, shape memory alloys, and ferroelectrics to electronically functional materials such as manganites. Interesting and unusual properties are found to be associated with these glasses and have potential for novel applications. Just as in prototypical spin glass and structural glasses, the elements of frustration and disorder lead to non-ergodocity, history dependence, frequency dependent relaxation behavior, and the presence of inhomogeneous nano clusters or domains. In addition, there are new states of matter, such as spin ice; however, it is still an open question as to whether these systems belong to the same family or universality class.The purpose of this work is to collect in a single volume the range of materials systems with differing functionalities that show many of the common characteristics of geometrical frustration, where interacting degrees of freedom do not fit in a lattice ormedium, and glassy behavior is accompanied by additional presence of disorder. The chapters are written by experts in their fields and span experiment and theory, as well as simulations.Frustrated Materials and Ferroic Glasseswill be of interest to a wide range of readers in condensed matter physics and materials science.…

Hardback. Condizione: New. 1st ed. 2016. 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'' toour toolkit to make the "Materials Genome'' a reality, the science of Materials Informatics.…

Taschenbuch. Condizione: Neu. Frustrated Materials and Ferroic Glasses | Turab Lookman (u. a.) | Taschenbuch | Springer Series in Materials Science | xiii | Englisch | 2019 | Springer | EAN 9783030072704 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

Condizione: New.

Hardcover. Condizione: Brand New. 292 pages. 9.25x6.10x0.83 inches. In Stock.

Paperback. Condizione: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Hardback. Condizione: New. 1st ed. 2016. 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'' toour toolkit to make the "Materials Genome'' a reality, the science of Materials Informatics.…

Taschenbuch. Condizione: Neu. Materials Discovery and Design | By Means of Data Science and Optimal Learning | Turab Lookman (u. a.) | Taschenbuch | Springer Series in Materials Science | xvi | Englisch | 2019 | Springer | EAN 9783030076023 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 ofinference and optimization methods in distilling the data and constraining predictions using insights and results from theory is key toachieving 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 ofin situspatially 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.…

Buch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 ofinference and optimization methods in distilling the data and constraining predictions using insights and results from theory is key toachieving 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 ofin situspatially 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.…

Condizione: New. Softcover reprint of the original 1st ed. 2018 edition NO-PA16APR2015-KAP.

Condizione: New. 1st ed. 2018 edition NO-PA16APR2015-KAP.

Taschenbuch. Condizione: Neu. Information Science for Materials Discovery and Design | Turab Lookman (u. a.) | Taschenbuch | Springer Series in Materials Science | xvii | Englisch | 2019 | Springer | EAN 9783319795416 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

Condizione: New. pp. 340.

Hardcover. Condizione: Brand New. 256 pages. 9.50x6.50x0.80 inches. In Stock.

Buch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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'' toour toolkit to make the 'Materials Genome'' a reality, the science of Materials Informatics.…

Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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'' toour toolkit to make the 'Materials Genome'' a reality, the science of Materials Informatics.…

Paperback. Condizione: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.