Researchers from Oak Ridge National Laboratory (ORNL) used high-throughput computational techniques to identify a new class of 2D nanomaterial, MXenes including boron-nitride.
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Researchers from University of California Riverside, Drexel, and Oak Ridge National Laboratory (ORNL) identified the atomistic mechanism by which MXenes degrade in water.
A collaborative team of researchers from Oak Ridge National Laboratory (ORNL) and four additional labs have published a new article in the Journal of Open Source Software paired with the release of a new version of the Cabana library for particle
Transformer language models provide state-of-the-art accuracy in a range of learning tasks, ranging from natural language processing to non-traditional applications such as molecular design.
Oak Ridge National Laboratory researchers developed an invertible neural network (INN) to effectively and efficiently solve earth-system model calibration and simulation problems.
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 the nano-length-scales of the long and short-period sup
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 team led by ORNL scientists developed a new method to estimate continuous-variable quantum states.
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.