Turab Lookman – författare

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’’ toour toolkit to make the “Materials Genome'''' a reality, the science of Materials Informatics.

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.