Supercomputing and Computation
Energy Frontier Research Centers (EFRC)
ORNL hosts two Energy Frontier Research Centers (EFRCs), the Fluid Interface Reactions, Structures, and Transport (FIRST) EFRC (http://www.ornl.gov/sci/first/index.shtml) and the Center for Defect Physics (CDP) EFRC (http://cdp.ornl.gov/). The aim of the former is to address the fundamental gaps in our current understanding of interfacial systems of high importance to future energy technologies, including electrical energy storage (batteries, supercapacitors) and heterogeneous catalysis for solar energy and solar fuels production. The latter is focused on structural materials, with an aim to bring a radically new level of rigor and insight to the discussion of defect structure, interactions, and dynamics in metals and alloys. Both EFRCs employ a combination of state of the art experimental and computational techniques.
Within the FIRST EFRC, we are working to develop improved molecular dynamics algorithms and implementations, specifically aimed to improve time to solution for first principles molecular dynamics calculations. Careful optimization and tuning of simulation codes has enabled, e.g., comprehensive studies of the of the liquid electrolytes used in lithium ion batteries with a time to solution acceptable to researchers.
Within the CDP EFRC, we are developing and applying state of the art electronic structure techniques to enable prediction of materials properties to unprecidented accuracy and confidence, using the Quantum Monte Carlo (QMC) technique. We recently performed extensive studies of the properties of aluminum metal and its defects (http://prb.aps.org/abstract/PRB/v85/i13/e134109), finding large (3000 Kelvin) errors in the predictions of the more commonly applied density functional methods for the formation energies of typical defects. This is significant because it is the defect properties that govern materials performance. The QMC calculations required several million CPU hours and are able to fully exploit the Leadership Computing machines at Oak Ridge. Due to the improvements in QMC algorithms and the improvements in available computer power, we expect to predict phase diagrams and defect properties of a wide range of materials within the next few years.