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.
