ITR: Large-scale, Grid-enabled Gaussian Orbital Implementation of Current Density and Spin Density Functional Theory for Ordered Systems

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This award is made in response to a proposal submitted to the Information Technology Research (ITR) Initiative. The objective of the research is to design, develop and implement, test, and apply major enhancements of the high-performance, all-electron, full-potential, relativistic gaussian-basis density functional theory (DFT) code for crystals, slabs, and periodic polymers called "GTOFF" (Gaussian Type Orbitals for Fitting Functions). The goals are (a) to include imposed magnetic fields as a major extension of materials-specific DFT prediction and interpretation of periodic systems; (b) make major speed-ups to handle much larger systems, e.g., nano-featured surfaces, ordered hard-soft interfaces; (c) introduce algorithms and data structures that will enable treatment of extremely large and/or complicated systems via grid technologies; (d) substantially improve the capability for revising and/or adding methods and capabilities to do more science.A major advantage of GTOFF is use of the same kinds of basis sets and procedures as the majority of molecular codes. It thus provides a seamless way to study chemically differentiated systems, from constituent atoms to molecule, cluster or nanoparticle to crystal or slab, including cluster-surface interactions and nano-featured surfaces.Successful, robust spin DFT algorithms in GTOFF will be reimplemented in C++ (from Fortran 77) to achieve modularity, manageability, and extendability now lacking. Those algorithms include generalized Ewald techniques to sum long-ranged contributions (rather than more common, sometimes less reliable multipole expansions), variational Coulomb fitting to eliminate 4-center integrals, fitting of exchange-correlation densities to fitted densities (eliminates extra sums over Brillouin Zone points), accuracy-conserving constraints on fittings, Douglas-Kroll-Hess relativity (including spin-orbit contributions). GTOFF now is serial. The redsign/reimplementation will exploit known parallelism opportunities as well as provide grid enablement. Current-density Functional Theory (C-DFT) will be built into the new design as a major extension to provide predictive treatment of materials at high magnetic field.The planned effort involves several major components: (a) redesign (not mere transcription) of the present GTOFF to take advantage of modern programming practices, construct a proper organization for parallelism (with hooks for MPI), include algorithms and data structures for grid-enabled computing; (b) design of gaussian orbital algorithms and techniques for current DFT; (c) re-implementation in C++ of the redesigned GTOFF for spin DFT; (d) extensive testing to assure reproducibility in serial mode, then in parallel; (e) implementation and testing of current DFT in the new GTOFF; (f) grid-enablement, including (but not limited to) interpolation among precalculated integral arrays, interpolation among approximate energy calculations, and energy gradient calculations direct from density fitting.The grant will provide support for a postdoctoral associate and graduate students who will will learn programming skills in addition to the fundamental chemistry and physics necessary to develop and apply the GTOFF codes.%%% This award is made in response to a proposal submitted to the Information Technology Research (ITR) Initiative. The objective of the research is to design, develop and implement, test, and apply major enhancements of the high-performance, all-electron, full-potential, relativistic gaussian-basis density functional theory (DFT) code for crystals, slabs, and periodic polymers called "GTOFF" (Gaussian Type Orbitals for Fitting Functions). The goals are (a) to include imposed magnetic fields as a major extension of materials-specific DFT prediction and interpretation of periodic systems; (b) make major speed-ups to handle much larger systems, e.g., nano-featured surfaces, ordered hard-soft interfaces; (c) introduce algorithms and data structures that will enable treatment of extremely large and/or complicated systems via grid technologies; (d) substantially improve the capability for revising and/or adding methods and capabilities to do more science.A major advantage of GTOFF is use of the same kinds of basis sets and procedures as the majority of molecular codes. It thus provides a seamless way to study chemically differentiated systems, from constituent atoms to molecule, cluster or nanoparticle to crystal or slab, including cluster-surface interactions and nano-featured surfaces. The grant will provide support for a postdoctoral associate and graduate students who will will learn programming skills in addition to the fundamental chemistry and physics necessary to develop and apply the GTOFF codes.***