91°µÍø

A multidisciplinary team of researchers from 91°µÍø (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 91°µÍø 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.

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 91°µÍø (ORNL) have developed a distributed implementation of graph convolutional neural networks [1].

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.