This measurement is correlated directly to ultrahigh energy-resolution monochromated electron energy-loss spectroscopy (EELS) measurements, which are able to directly measure the phonon response at t
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Multimodel ensembling improves predictions and considers model uncertainties. In this study, we present a Bayesian Neural Network (BNN) ensemble approach for large-scale precipitation predictions based on a set of climate models.
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.
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.
Deep learning models have trained to predict crystallographic and thermodynamic properties of multi-component solid solution alloys, enabling the design of advanced alloys.