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Author: Krishna Rajan
Publisher: Butterworth-Heinemann
ISBN: 012394614X
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: Krishna Rajan
Publisher: Butterworth-Heinemann
ISBN: 012394614X
Category : Technology & Engineering
Languages : en
Pages : 542
Get Book Here
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: 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: Nirupam Chakraborti
Publisher: Elsevier Inc. Chapters
ISBN: 0128059354
Category : Technology & Engineering
Languages : en
Pages : 42
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Book Description
Artificial neural networks (ANNs) and genetic programming (GP) have already emerged as two very effective computing strategies for constructing data-driven models for systems of scientific and engineering interest. However, coming up with accurate models or meta-models from noisy real-life data is often a formidable task due to their frequent association with high degrees of random noise, which might render an ANN or GP model either over- or underfitted. This problem has recently been tackled in two emerging algorithms, Evolutionary Neural Net (EvoNN) and Bi-objective Genetic Programming (BioGP), which utilize the concept of Pareto tradeoff and apply a bi-objective genetic algorithm (GA) in the basic framework of both ANNs and GP. These concepts are elaborated in detail in this chapter.
Author: James N. Cawse
Publisher: Elsevier Inc. Chapters
ISBN: 0128059397
Category : Technology & Engineering
Languages : en
Pages : 47
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Book Description
Design of combinatorial and high-throughput experiments has continued to build on the progress of the last two decades. New variations of factorial and mixture designs have expanded their capability. Increasing attention is being paid to adapting designs to the constraints of the physical apparatus, in the form of split-plot methods or conscious understanding of the statistical penalties to be paid. Rapidly increasing computer power has allowed the use of more sophisticated algorithmic designs and evolutionary methods. Finally, descriptor-based design and analysis of data is making steady progress and there are hopes of its reaching a mature state in the coming decade.
Author: Krishna Rajan
Publisher: Elsevier Inc. Chapters
ISBN: 0128059311
Category : Technology & Engineering
Languages : en
Pages : 32
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Book Description
Studying structure–property relationships is an accepted paradigm in materials science, yet these relationships are often not linear and the challenge is to seek patterns among multiple length and time scales. There is rarely a single multiscale theory or experiment that can meaningfully and accurately capture such information. In this chapter we introduce the rationale as to why we need informatics by briefly summarizing the challenges of information complexity one has to deal with in material science, in order to systematically establish structure–property–processing relationships. Some of the concepts and topics to be covered in this book are introduced, including information networks, data mining, databases, and combinatorial experiments to mention a few. The value of “materials informatics” lies in its ability to permit one to survey complex, multiscale information in a high-throughput, statistically robust and yet physically meaningful manner.
Author: Laurent A. Baumes
Publisher: Elsevier Inc. Chapters
ISBN: 0128059435
Category : Technology & Engineering
Languages : en
Pages : 51
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Book Description
Very recently, the design and understanding of materials synthesis have received considerable attention where modeling approaches are decisive. Here, we focus on the generation of crystalline inorganic frameworks. Despite high-throughput (HT) methods having proved to be useful for the discovery of zeolites, the determination of the new phases’ structure takes up a large part of the entire process. Therefore, we show how graphic processing units (GPUs) can be used in order to speed up this mandatory step. We describe GPUs and predictive methods for phase determination. Then, we show all the details that allow us to reach a stable and robust solution with benchmark analysis and real applications for zeolites.
Author: Ke Wu
Publisher: Elsevier Inc. Chapters
ISBN: 012805946X
Category : Technology & Engineering
Languages : en
Pages : 53
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Book Description
The Materials Genome Initiative (MGI) was conceived as a unified effort to capture, curate, and exploit materials structure/property information on a grand scale to enable rapid, cost-effective development of novel materials with predictable properties. While the use of “genomic” methods to facilitate property prediction, virtual design, and discovery of materials is relatively new, the concepts driving the development of materials informatics are based, solidly, on the lessons learned during the development history of cheminformatics and bioinformatics. This chapter describes some of the ways in which cheminformatics and machine learning methods have been adapted for, and utilized in, materials science and engineering applications. Examples of how materials quantitative structure–property relationship (MQSPR) models are created, validated, and utilized are presented.
Author: Radislav Potyrailo
Publisher: Elsevier Inc. Chapters
ISBN: 0128059427
Category : Technology & Engineering
Languages : en
Pages : 62
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Book Description
Sensing materials play a critical role in advancing selectivity, response speed, and sensitivity of gas determinations with sensors. The desirable capabilities of sensing materials originate from their numerous functional parameters, which can be tailored to meet specific sensing needs. By increasing the structural and functional complexity of sensing materials, the ability to rationally define the precise requirements that will result in desired materials properties becomes increasingly limited. Combinatorial experimentation methodologies impact all areas of sensing materials research. This chapter demonstrates the broad applicability of combinatorial technologies in discovery and optimization of new sensing materials for gas detection. We discuss general principles of combinatorial materials screening, followed by the discussion of the opportunities facilitated by combinatorial technologies for discovery and optimization of new sensing materials. We further critically analyze results of sensing materials development using discrete and gradient materials arrays. The emphasis of this chapter is on the analysis of results focused on the improvements of sensor selectivity and long-term stability of sensing materials. Examples from a wide variety of sensors based on various energy-transduction principles that involve radiant, mechanical, and electrical types of energy demonstrate the impact of combinatorial methodologies across diverse kinds of sensing materials.
Author: Surya R. Kalidindi
Publisher: Elsevier
ISBN: 012410455X
Category : Technology & Engineering
Languages : en
Pages : 230
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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: Joseph Glick
Publisher: Elsevier Inc. Chapters
ISBN: 0128059389
Category : Technology & Engineering
Languages : en
Pages : 49
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Book Description
The discipline of informatics emerged from the need to translate biomedical research into evidence-based healthcare protocols and policy. Materials science informatics is rooted in an analogous need to “translate” physical sciences research and discoveries into materials-based solutions to address a broad range of issues and challenges for business, government, and the environment. Ontologies and databases are key elements of translational architectures and therefore are fundamental tools of the practice of informatics. Databases are tools for engineering data and information, while ontologies are tools for engineering knowledge and utility. Since knowledge and utility are the core objectives of informatics, correctly understanding and utilizing ontologies is critical to the development of effective materials informatics programs and tools. Rooted in philosophy, the term ontology appears most frequently today in connection with semantic web technology, where it refers to vocabularies used by inference engines to interpret human use of language. Materials science ontologies need to capture the scientific context of the defined concepts to support modeling and prediction of multidimensional structure–property relationships in variable environments and applications. Addressing the complexity of materials science ontologies requires a significant departure from traditional database and semantic web ontology approaches, including the use of neural networks that are capable of implementing methods for modeling context, relevance, complex systems, and human expertise. Pioneering efforts in this space include the Knowledge Engineering for Nanoinformatics Pilot (KENI) launched by the Nanoinformatics Society in 2010, and a collaborative Materials Genome Modeling Methodology initiative led by Iowa State University and initiated in 2011.
