Supercomputing and Computation



This project uses ab-initio many-body electronic structure calculations to unravel outstanding problems in the prediction of materials properties of interest to DOE. In particular, we are developing an understanding of metal oxides that have wide application including energy storage, catalysis, and energy production, and metals that are widely used as structural materials. This is achieved by using a state of the art electronic structure method, Quantum Monte Carlo (QMC), implemented in QMCPACK. To our knowledge, this is the only QMC code fully optimized and proven to run on the hybrid GPU-CPU Titan architecture at OLCF. The package utilizes modern CUDA, C++, XML, HDF5, MPI and OpenMP and implements state of the art algorithms. The package is fully open source. Significant developments in the algorithms and code have been recently implemented and the code has been fully "GPUized" with a large number of custom CUDA kernels written and tested on titandev. We aim to both solve scientific problems in these materials and to identify the fundamental problems limiting accuracy with density functional theory approaches. Thus, this project targets significant scientific impact and a longer lasting impact in the materials modeling community.Diagonalization Solvers for Electronic Collective Phenomena in Nanoscience.  The objective of this project is to study collective phenomena at the electronic level, using the DMRG algorithm, and to develop and make available the corresponding computational codes. The proposed research will be carried out for realistic models of relevance for practical applications of strongly correlated electronic materials. We aim to understand three aspects: the real time evolution in electron transport, the temperature dependence of electronic properties in nanostructures, and the multiscale nature of the emerging orders in strongly correlated systems. The main approach of these theoretical studies is the use of the density matrix renormalization group algorithm to obtain information from the models. We use also auxiliary approaches, suchas the Lanczos and Davidson algorithm, and for testing purposes either series expansions, or the non-interacting case when available and meaningful.


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