First-principles data for solid solution niobium-tantalum-vanadium alloys with body-centered-cubic structures.

We present four open-source datasets that provide results of density functional theory (DFT) calculations of ground-state properties of refractory solid solution binary alloys niobium-tantalum (NbTa), niobium-vanadium (NbV), tantalum-vanadium (TaV), and ternary alloys NbTaV ordered in body-centered-cubic (BCC) structures with 128 Bravais lattice sites. The first-principles code used to run the calculations is the Vienna Ab-Initio Simulation Package. The calculations have been collected by uniformly sampling chemical compositions across the entire compositional range. For each chemical composition, the calculations have been run for 100 randomized arrangements of the constituents on the BCC lattice sites. This sampling methodology resulted in running DFT simulations for a total of 3,100 randomized atomic configurations over 31 chemical compositions for each of the three binary alloys Nb-Ta, Nb-V, Ta-V, and a total of 10,500 randomized atomic structures over 105 chemical compositions for the ternary alloys Nb-Ta-V. For each atomic configuration, geometry optimization has been performed, and the data released contains information about each step of geometry optimization for each atomic configuration.

Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules.

Graph Convolutional Neural Network (GCNN) is a popular class of deep learning (DL) models in material science to predict material properties from the graph representation of molecular structures. Training an accurate and comprehensive GCNN surrogate for molecular design requires large-scale graph datasets and is usually a time-consuming process. Recent advances in GPUs and distributed computing open a path to reduce the computational cost for GCNN training effectively. However, efficient utilization of high performance computing (HPC) resources for training requires simultaneously optimizing large-scale data management and scalable stochastic batched optimization techniques. In this work, we focus on building GCNN models on HPC systems to predict material properties of millions of molecules. We use HydraGNN, our in-house library for large-scale GCNN training, leveraging distributed data parallelism in PyTorch. We use ADIOS, a high-performance data management framework for efficient storage and reading of large molecular graph data. We perform parallel training on two open-source large-scale graph datasets to build a GCNN predictor for an important quantum property known as the HOMO-LUMO gap. We measure the scalability, accuracy, and convergence of our approach on two DOE supercomputers: the Summit supercomputer at the Oak Ridge Leadership Computing Facility (OLCF) and the Perlmutter system at the National Energy Research Scientific Computing Center (NERSC). We present our experimental results with HydraGNN showing (i) reduction of data loading time up to 4.2 times compared with a conventional method and (ii) linear scaling performance for training up to 1024 GPUs on both Summit and Perlmutter.

Anti-scratching behavioral effect of the essential oil and phytol isolated from Artemisia princeps Pamp. in mice.

The anti-scratching behavioral effect of the essential oil and phytol isolated from Artemisia princeps Pamp. (AP, family Asteraceae), which is widely used in traditional medicine for inflammatory diseases, was investigated IN VIVO. Treatment of mice with AP essential oil (APEO) and phytol inhibited histamine- and compound 48/80-induced scratching behaviors. The anti-scratching behavioral effects of APEO and phytol are in proportion to their vascular permeability-inhibitory effects. These agents also inhibited the level of allergic cytokines, IL-4, and TNF- α, and the activation of transcription factors, NF- κB and c-jun (AP-1), in histamine-treated skin tissues. Based on these results, APEO and phytol may improve scratching behavior in skin by inhibiting the expression of allergic cytokines via the regulation of NF- κB and AP-1 activation.

Hybrid cloud and cluster computing paradigms for life science applications.

BACKGROUND: Clouds and MapReduce have shown themselves to be a broadly useful approach to scientific computing especially for parallel data intensive applications. However they have limited applicability to some areas such as data mining because MapReduce has poor performance on problems with an iterative structure present in the linear algebra that underlies much data analysis. Such problems can be run efficiently on clusters using MPI leading to a hybrid cloud and cluster environment. This motivates the design and implementation of an open source Iterative MapReduce system Twister. RESULTS: Comparisons of Amazon, Azure, and traditional Linux and Windows environments on common applications have shown encouraging performance and usability comparisons in several important non iterative cases. These are linked to MPI applications for final stages of the data analysis. Further we have released the open source Twister Iterative MapReduce and benchmarked it against basic MapReduce (Hadoop) and MPI in information retrieval and life sciences applications. CONCLUSIONS: The hybrid cloud (MapReduce) and cluster (MPI) approach offers an attractive production environment while Twister promises a uniform programming environment for many Life Sciences applications. METHODS: We used commercial clouds Amazon and Azure and the NSF resource FutureGrid to perform detailed comparisons and evaluations of different approaches to data intensive computing. Several applications were developed in MPI, MapReduce and Twister in these different environments.