Scalable Quantum Monte Carlo for Novel Materials Design

Pozun Zachary D, Austin TX

Investigators

Abstract

This project will improve the scalability of quantum Monte Carlo (QMC) methods to better exploit modern cyberinfrastructure (CI) in finding very accurate energies of chemical and material systems. The research will include a collaborative effort across multiple disciplines to develop a linear scaling QMC algorithm suitable for parallelization at the petascale. This new QMC method will allow complex materials systems to be characterized theoretically with a high degree of accuracy. Presently, density functional theory (DFT) is used for large scale material modeling despite problems caused by electron self-energy interactions and the failure to describe long-range dispersion; however, modern CI has recently made QMC calculations of real materials tractable. The proposed research will exploit the use of localized basis functions and orbitals in order to reduce the computational cost of QMC calculations and to apply QMC to larger molecular and materials systems. The project will also integrate education, career development, and research. Through a combination of curriculum development and outreach, the integration of computational chemistry into chemistry curricula will be promoted. The outreach program partners with the PSC in order to promote the participation of underrepresented groups in the computational sciences. The plan develops a broad educational infrastructure in computational science that is suited to students ranging from K-12 through advanced undergraduates. By extending the applicability of QMC, computational scientists will be able to use CI in order to design new solar cells and other alternative energy materials, model the behavior of molecules adsorbing at interfaces, and study highly-correlated semiconductors. The algorithms will be included in a freely available QMC code, CASINO, in order to promote their usage.