A multidisciplinary team of researchers from Oak Ridge National Laboratory (ORNL) and the University of Texas at Austin developed a new framework for assessing the accuracy of approximate models of microstructure formation.
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Automated experiments in Scanning Transmission Electron Microscopy (STEM) are implemented for rapid discovery of local structures, symmetry-breaking distortions, and internal electric and magnetic fields in complex materials.
A team of researchers from Oak Ridge National Laboratory demonstrated highly scalable performance across thousands of GPUs in a newly released version of the open-source MEUMAPPS phase-field simulation framework.
This work develops an approach for engineering non-Gaussian photonic states in discrete frequency bins.
Using first-principles calculations and group-theory-based models, we study the stabilization of ferrielectricity (FiE) in CuInP2Se6.
Simulations of Inconel 625 microstructure development and constitutive properties during Selective Laser Melting processing were performed utilizing two exascale-capable codes on the pre-exascale Summit supercomputer.
Deep learning models have trained to predict crystallographic and thermodynamic properties of multi-component solid solution alloys, enabling the design of advanced alloys.
Researchers from the Computing and Computational Sciences Directorate (CCSD) at Oak Ridge National Laboratory (ORNL) have developed a distributed implementation of graph convolutional neural networks [1].