First-principles calculations of the electronic structure of open-shell condensed matter systems

We develop a Green's function approach to quasiparticle excitations of open-shell systems within the GW approximation. It is shown that accurate calculations of the characteristic multiplet structure require a precise knowledge of the self energy and, in particular, its poles. We achieve this by constructing the self energy from appropriately chosen mean-field theories on a fine frequency grid. We apply our method to a two-site Hubbard model, several molecules and the negatively charged nitrogen-vacancy defect in diamond, and obtain good agreement with experiment and other high-level theories.Comment: 5 page

Exascale Deep Learning for Climate Analytics

We extract pixel-level masks of extreme weather patterns using variants of Tiramisu and DeepLabv3+ neural networks. We describe improvements to the software frameworks, input pipeline, and the network training algorithms necessary to efficiently scale deep learning on the Piz Daint and Summit systems. The Tiramisu network scales to 5300 P100 GPUs with a sustained throughput of 21.0 PF/s and parallel efficiency of 79.0%. DeepLabv3+ scales up to 27360 V100 GPUs with a sustained throughput of 325.8 PF/s and a parallel efficiency of 90.7% in single precision. By taking advantage of the FP16 Tensor Cores, a half-precision version of the DeepLabv3+ network achieves a peak and sustained throughput of 1.13 EF/s and 999.0 PF/s respectively.Comment: 12 pages, 5 tables, 4, figures, Super Computing Conference November 11-16, 2018, Dallas, TX, US

Galactos: Computing the Anisotropic 3-Point Correlation Function for 2 Billion Galaxies

The nature of dark energy and the complete theory of gravity are two central questions currently facing cosmology. A vital tool for addressing them is the 3-point correlation function (3PCF), which probes deviations from a spatially random distribution of galaxies. However, the 3PCF's formidable computational expense has prevented its application to astronomical surveys comprising millions to billions of galaxies. We present Galactos, a high-performance implementation of a novel, O(N^2) algorithm that uses a load-balanced k-d tree and spherical harmonic expansions to compute the anisotropic 3PCF. Our implementation is optimized for the Intel Xeon Phi architecture, exploiting SIMD parallelism, instruction and thread concurrency, and significant L1 and L2 cache reuse, reaching 39% of peak performance on a single node. Galactos scales to the full Cori system, achieving 9.8PF (peak) and 5.06PF (sustained) across 9636 nodes, making the 3PCF easily computable for all galaxies in the observable universe.Comment: 11 pages, 7 figures, accepted to SuperComputing 201

Many-body interactions in quasi-freestanding graphene

The Landau-Fermi liquid picture for quasiparticles assumes that charge carriers are dressed by many-body interactions, forming one of the fundamental theories of solids. Whether this picture still holds for a semimetal like graphene at the neutrality point, i.e., when the chemical potential coincides with the Dirac point energy, is one of the long-standing puzzles in this field. Here we present such a study in quasi-freestanding graphene by using high-resolution angle-resolved photoemission spectroscopy. We see the electron-electron and electron-phonon interactions go through substantial changes when the semimetallic regime is approached, including renormalizations due to strong electron-electron interactions with similarities to marginal Fermi liquid behavior. These findings set a new benchmark in our understanding of many-body physics in graphene and a variety of novel materials with Dirac fermions.Comment: PNAS 2011 ; published ahead of print June 27, 201

A molecular-MNIST dataset for machine learning study on diffraction imaging and microscopy

An image dataset of 10 different size molecules, where each molecule has 2,000 structural variants, is generated from the 2D cross-sectional projection of Molecular Dynamics trajectories. The purpose of this dataset is to provide a benchmark dataset for the increasing need of machine learning, deep learning and image processing on the study of scattering, imaging and microscopy

Long-term warming alters the composition of Arctic soil microbial communities

Despite the importance of Arctic soils in the global carbon cycle, we know very little of the impacts of warming on the soil microbial communities that drive carbon and nutrient cycling in these ecosystems. Over a 2-year period, we monitored the structure of soil fungal and bacterial communities in organic and mineral soil horizons in plots warmed by greenhouses for 18 years and in control plots. We found that microbial communities were stable over time but strongly structured by warming. Warming led to significant reductions in the evenness of bacterial communities, while the evenness of fungal communities increased significantly. These patterns were strongest in the organic horizon, where temperature change was greatest and were associated with a significant increase in the dominance of the Actinobacteria and significant reductions in the Gemmatimonadaceae and the Proteobacteria. Greater evenness of the fungal community with warming was associated with significant increases in the ectomycorrhizal fungi, Russula spp., Cortinarius spp., and members of the Helotiales suggesting that increased growth of the shrub Betula nana was an important mechanism driving this change. The shifts in soil microbial community structure appear sufficient to account for warming-induced changes in nutrient cycling in Arctic tundra as climate warm