Speeding up GW quasiparticle calculations to meet the challenge of fast and accurate materials prediction

NON-TECHNICAL SUMMARY Designing novel materials with desired properties is of vital importance for solving some of the most pressing challenges facing our society in energy and environmental issues. Computational materials design relies on theoretical and computational methods that can reliably predict materials properties within a reasonable time frame for both their lowest-energy state and excited states. Electronic structure methods based on Density Functional Theory are capable of predicting many important lowest-energy state properties, and the so-called "GW method" is by far the most successful and theoretically sound method for predicting excited-state properties of materials. Unfortunately, despite celebrated advances, accurate and efficient predictions of excited-state properties of complex solid systems remain a major challenge. This project supports theoretical and computational research and education that will significantly reduce the computational time and required memory of GW calculations for complex systems such as nanostructures, complex compounds, surfaces, interfaces, and materials containing spatially localized electrons. This will enable fast and accurate predictions for excited-state properties of a significantly wide range of scientifically and technologically important materials. Research will be performed in collaboration with both domestic and international groups. The developed code will first be made available to interested research groups, and then will be released to public after validation and optimization. This project will provide interdisciplinary training in physics, computational materials science, and high performance computing for both graduate and undergraduate students. Such integrated training will greatly broaden students' knowledge base and skill sets in preparation for their future career. TECHNICAL SUMMARY Accurate predictions of excited states properties are critical for computational screening and design of materials for energy and electronics applications. Unfortunately, despite much research effort and celebrated advances, notably the development of first-principles GW methods, accurate and efficient predictions of excited-state properties of solids remain a major challenge. This is particularly true for systems with large unit cells, such as nanostructures, complex multinary compounds, surfaces, interfaces, and materials containing localized electrons due to the unfavorable scaling of the computational cost of GW calculations with respect to the system size. This project supports theoretical and computational research and education which involves the development of several new techniques that will dramatically reduce the computational cost of GW calculations for large complex systems. These new approaches include a) A Fourier filtering technique for drastically reducing the storage and computation cost associated with high-energy states; b) An energy-integration approach for alleviating the burden of explicit band-by-band summation in conventional GW calculations; c) A novel strategy for reducing the computational and memory requirement of the dielectric matrix; and d) Implementation of diagonalization methods that calculate only those eigenstates at or near pre-determined energies for GW calculations. These new approaches, once fully developed and integrated, are expected to result in over two orders of magnitude reduction in computational time and memory requirement for GW calculations on large systems. Research will be performed in collaboration with both domestic and international groups. The developed code will first be made available to interested research groups, and then will be released to public after validation and optimization. This project will provide interdisciplinary training in physics, computational materials science, and high performance computing for both graduate and undergraduate students. Such integrated training will greatly broaden students' knowledge base and skill sets in preparation for their future career.