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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.
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.
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 or
medium, 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 Glasses will be of interest to a wide range
of readers in condensed matter physics and materials science.
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.
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.
Machine learning methods are changing the way we design and
discover new materials. This book provides an overview of
approaches successfully used in addressing materials problems
(alloys, ferroelectrics, dielectrics) with a focus on probabilistic
methods, such as Gaussian processes, to accurately estimate density
functions. The authors, who have extensive experience in this
interdisciplinary field, discuss generalizations where more than
one competing material property is involved or data with differing
degrees of precision/costs or fidelity/expense needs to be
considered.
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 or
medium, 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 Glasses will be of interest to a wide range
of readers in condensed matter physics and materials science.
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