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 Oak Ridge National Laboratory (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.
Large-scale numerical calculations reveal fluctuating spin and charge stripes intertwined with pair-density-wave
A research team from ORNL, Pacific Northwest National Laboratory, and Arizona State University has developed a novel method to detect out-of-distribution (OOD) samples in continual learning without forgetting the learned knowledge of preceding tasks.
