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Information Science for Materials Discovery and Design (Hardcover, 1st ed. 2016)
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Information Science for Materials Discovery and Design (Hardcover, 1st ed. 2016)
Series: Springer Series in Materials Science, 225
Expected to ship within 12 - 17 working days
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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.
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