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Author: Turab Lookman
Publisher: Springer
ISBN: 331923871X
Category : Technology & Engineering
Languages : en
Pages : 316
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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: 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: Phil De Luna
Publisher: Walter de Gruyter GmbH & Co KG
ISBN: 3110738082
Category : Computers
Languages : en
Pages : 215
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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: Geoffrey A Ozin
Publisher: Royal Society of Chemistry
ISBN: 1839163836
Category : Science
Languages : en
Pages : 464
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Book Description
Materials have the potential to be the centrepiece for the transition to viable renewable energy technologies if they realise a specific suite of properties and achieve a desired set of performance metrics. The envisioned transition involves the discovery of materials that enable generation, conversion, storage, transmission, and utilization of renewable energy. This book presents, through the eye of materials chemistry, an umbrella view of the myriad of classes of materials that make renewable energy technologies work. They are poised to facilitate the transition of non-renewable and unsustainable energy systems of the past into renewable and sustainable energy systems of the future. It is a story that often begins in chemistry laboratories with the discovery of new energy materials. Yet, to displace materials in existing energy technologies with new ones, depends not only on the ability to design and engineer a superior set of performance metrics for the material and the technology but also the requirement to meet a demanding collection of economic, regulatory, social, policy, environmental and sustainability criteria. Disruption in the traditional way of discovering materials is coming with the emergence of artificial intelligence, machine learning and robotic automation designed to accelerate the well-established discovery process, massive libraries of materials can be evaluated and the possibilities are endless. This book provides a perspective on the application of these new technologies to this field as well as an overview of energy materials discovery in the broader techno-economic and social context. Any budding researcher or more experienced materials scientist will find a guide to a fascinating story of discovery and emerge with a vision of what is next.
Author: N. M. Anoop Krishnan
Publisher: Springer Nature
ISBN: 3031446224
Category :
Languages : en
Pages : 287
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Book Description
Author: Turab Lookman
Publisher: Springer
ISBN: 3319994654
Category : Science
Languages : en
Pages : 266
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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: Inamuddin
Publisher: Materials Research Forum LLC
ISBN: 1644902532
Category : Technology & Engineering
Languages : en
Pages : 147
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Book Description
The book is concerned with the use of Artificial Intelligence in the discovery, production and application of new engineering materials. Topics covered include nano-robots. data mining, solar energy systems, materials genomics, polymer manufacturing, and energy conversion issues. Keywords: Artificial Intelligence, Mathematical Models, Machine Learning, Artificial Neural Networks, Bayesian Analysis, Vector Machines, Heuristics, Crystal Structure, Component Prediction, Process Optimization, Density Functional Theory, Monitoring, Classification, Nano-Robots, Data Mining, Solar Photovoltaics, Renewable Energy Systems, Alternative Energy Sources, Material Genomics, Polymer Manufacturing, Energy Conversion.
Author: Artem Oganov
Publisher: Royal Society of Chemistry
ISBN: 1782629610
Category : Science
Languages : en
Pages : 470
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Book Description
A unique and timely book providing an overview of both the methodologies and applications of computational materials design.
Author: Krishna Rajan
Publisher: Butterworth-Heinemann
ISBN: 9780128101216
Category : Technology & Engineering
Languages : en
Pages : 542
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Book Description
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 "
Author: Phil De Luna
Publisher: Walter de Gruyter GmbH & Co KG
ISBN: 3110733250
Category : Computers
Languages : en
Pages : 235
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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: Artem R Oganov
Publisher: Royal Society of Chemistry
ISBN: 1788015622
Category : Science
Languages : en
Pages : 470
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Book Description
New technologies are made possible by new materials, and until recently new materials could only be discovered experimentally. Recent advances in solving the crystal structure prediction problem means that the computational design of materials is now a reality. Computational Materials Discovery provides a comprehensive review of this field covering different computational methodologies as well as specific applications of materials design. The book starts by illustrating how and why first-principle calculations have gained importance in the process of materials discovery. The book is then split into three sections, the first exploring different approaches and ideas including crystal structure prediction from evolutionary approaches, data mining methods and applications of machine learning. Section two then looks at examples of designing specific functional materials with special technological relevance for example photovoltaic materials, superconducting materials, topological insulators and thermoelectric materials. The final section considers recent developments in creating low-dimensional materials. With contributions from pioneers and leaders in the field, this unique and timely book provides a convenient entry point for graduate students, researchers and industrial scientists on both the methodologies and applications of the computational design of materials.
