Roadmap on Machine learning in electronic structure

AbstractIn recent years, we have been witnessing a paradigm shift in computational materials science. In fact, traditional methods, mostly developed in the second half of the XXth century, are being complemented, extended, and sometimes even completely replaced by faster, simpler, and often more accurate approaches. The new approaches, that we collectively label by machine learning, have their origins in the fields of informatics and artificial intelligence, but are making rapid inroads in all other branches of science. With this in mind, this Roadmap article, consisting of multiple contributions from experts across the field, discusses the use of machine learning in materials science, and share perspectives on current and future challenges in problems as diverse as the prediction of materials properties, the construction of force-fields, the development of exchange correlation functionals for density-functional theory, the solution of the many-body problem, and more. In spite of the already numerous and exciting success stories, we are just at the beginning of a long path that will reshape materials science for the many challenges of the XXIth century

Roadmap on Machine learning in electronic structure

In recent years, we have been witnessing a paradigm shift in computational materials science. In fact, traditional methods, mostly developed in the second half of the XXth century, are being complemented, extended, and sometimes even completely replaced by faster, simpler, and often more accurate approaches. The new approaches, that we collectively label by machine learning, have their origins in the fields of informatics and artificial intelligence, but are making rapid inroads in all other branches of science. With this in mind, this Roadmap article, consisting of multiple contributions from experts across the field, discusses the use of machine learning in materials science, and share perspectives on current and future challenges in problems as diverse as the prediction of materials properties, the construction of force-fields, the development of exchange correlation functionals for density-functional theory, the solution of the many-body problem, and more. In spite of the already numerous and exciting success stories, we are just at the beginning of a long path that will reshape materials science for the many challenges of the XXIth century.</p

Improvement in mechanical properties through structural hierarchies in bio-inspired materials

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Materials Science and Engineering, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 155-169).Structural biological materials such as bone, nacre, insect cuticle, and sea sponge exoskeleton showcase the use of inferior building blocks like proteins and minerals to create structures that afford load-bearing and armor capabilities. Many of these are composite structures that possess the best of the properties of their base constituents. This is in contrast to many engineering materials, such as metals, alloys, ceramics and their composites which show improvement in one mechanical property (e.g. stiffness) at the cost of another disparate one (e.g. toughness). These excellent design examples from biology raise questions about whether similar design., and improvement in disparate properties, can be achieved using common engineering materials. The identification of broad design principles that can be transferred from biological materials to structural design, and the analysis of the utility of these principles have been missing in literature. In this thesis, we have firstly identified certain universal features of design of biological structures for mimicking with engineering materials: a) presence of geometric design at the nanoscale, b) the use of mechanically inferior building blocks, and c) the use of structural hierarchies from the nanoscale to the macroscale. We firstly design. in silico, metal-matrix nanocomposites, mimicking the geometric design found at the nanoscale in bone. We show this leads to improvements in flow strength of the material. A key finding is that limiting values of certain of these parameters shuts down dislocation-mediated plasticity leading to peak in flow strength of the structure. Metals are however, costly constituents, and we next confront the issue of whether it is possible to use a single mechanically inferior and commonly available constituent, such as silica, to create superior bioinspired structures. We turn to diatom exoskeletons, protective armor structures for algae made almost entirely of silica, and create nanoporous silica structures inspired from their geometry. We show large improvements in ductility of silica through this design, facilitated by a key size-dependent brittle-to-ductile deformation transition in these structures. Nanostructuring, while improving ductility, affects the stiffness of these structures, softening them by up to 90% of bulk silica. Hierarchical assembly of silica structures is then used to regain the stiffness lost due to nanostructuring while not losing their improvement in toughness. Finally, improvement in toughness with several levels of hierarchy is studied, to showcase a defect-tolerant behavior that arises with the addition of hierarchies, i.e., tolerance of the fracture strength to a wide range of sizes of cracks present in the structure. The importance of R-curve behavior, i.e., toughness change with the advance of a crack in the structure. to the defect-tolerance length scale is also established. These findings showcase the validity of using design principles obtained from biological materials for improvement in mechanical properties of engineering materials.by Dipanjan Sen.Ph.D