The researchers from ORNL have developed a new and faster algorithm for the graph all-pair shortest-path (APSP) problem.
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A team of ORNL researchers has used the DCA++ application, a popular code for predicting the performance of quantum materials, to verify two performance-enhancing strategies.
Kokkos is a programming model and library for writing performance-portable code in C++.
A new method was developed for the discovery of fundamental descriptors for gas adsorption through deep learning neural network (DNN) approach. This approach has great potential to identify structural parameters for gas adsorption.
Researchers at ORNL have developed new solvers for implicit time discretization of a simplified Boltzmann-Poisson system.
A learning-based approximation strategy has been developed to accelerate parameter studies for non-classical models of diffusion.
Developed a deep-learning approach to automatically create libraries of structural and electronic properties of atomic defects in 2D materials.
Direct experimental evidence of gas-phase methyl radicals in propane oxidative dehydrogenation (ODHP) combined with density functional theory (DFT) calculations uncovers the mechanism behind the exceptional selectivity to olefins over BN catalysts