NONTECHNICAL SUMMARYDiscoveries of new functional materials are crucial for technology advancement as well as for economic development, and advanced computational methods are accelerating progress in this area. This award supports research and education towards the acceleration of progress in our understanding of complex materials, which manifest prominent competition between quantum effects such as magnetism, superconductivity, and strongly correlated electron behavior.In simpler materials, for which model representations in terms of a system of independent particles suffices, advanced methods have enabled large-scale simulations of the physical properties of realistic systems. For materials exhibiting correlated electron behavior, where the behavior of one electron is strongly dependent on that of other electrons in the material, the Dynamical Mean Field Theory method has enabled practical and accurate calculations of basic material properties. This project focuses on the development of software that simulates such complex materials with a computer, and which can predict material properties. The focus is in improving the precision of the theory, and in developing new theoretical spectroscopy tools, which will enable the prediction of the precise crystal structures of correlated solids, and the prediction of actual spectroscopic measurements made using neutron and x-ray scattering experimental techniques.The project will lead to the development of algorithms and software that will be incorporated in open-source code packages, which subsequently will be made available to the wider research community. These tools will help the materials science community to find promising material candidates for synthesis, which should further enable theory-assisted material discovery and design. Training and mentorship of junior researchers will also take place as part of the project, contributing to the development of scientific workforce.TECHNICAL SUMMARY: In the search for new materials with enhanced physical properties it is crucial to develop broad capabilities for computational characterization. This award supports research and education towards the development of a number of theoretical spectroscopic tools, which can be used in combination with electronic structure tools. The latter are based on Dynamical Mean Field Theory (developed under previous NSF support), and enable theoretical prediction of material properties using first-principles methods. The spectroscopic tools and methods that will be developed in this project include: i) relaxation of complex crystal structures using forces on all atoms in the unit cell, which will enable prediction of complex crystal structures; ii) extension of the calculation of forces to materials with large spin-orbit coupling; iii) developing a tool to predict phase transitions in systems with strongly coupled crystal- and electronic structure; iv) developing a tool to calculate phonons in correlated materials; v) developing x-ray scattering spectroscopy that properly takes into account the core-hole interaction.These tools will be made available to the broader scientific community as they will be incorporated in the PI's electronic structure software package, which is widely used, and is distributed as open-source; see: http://hauleweb.rutgers.edu/tutorials/. The overall goal of the research is to build a predictive framework for describing the physical properties of correlated materials, to experimentally validate it, by close collaboration with material scientists to test the predictions of the computational theory, and to improve it in the areas of disagreement. Training and mentorship of junior researchers will also take place as part of the project, contributing to the development of scientific workforce.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
|Effective start/end date||9/1/18 → 8/31/21|
- National Science Foundation (National Science Foundation (NSF))
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