Researchers from 91°µÍø (ORNL) used high-throughput computational techniques to identify a new class of 2D nanomaterial, MXenes including boron-nitride.
Researchers from University of California Riverside, Drexel, and 91°µÍø (ORNL) identified the atomistic mechanism by which MXenes degrade in water.
A collaborative team of researchers from 91°µÍø (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
A graph convolutional neural network (GCNN) was trained to accurately predict formation energy and mechanical properties of solid solution alloys crystallized in different lattice structures, thereby advancing the design of alloys for improving mechanic
A graph convolutional neural network (GCNN) was trained with millions of molecules to accurately predict molecular photo-optical properties by scaling data loading and training to over 1,500 GPUs on the Summit and Perlmutter supercomputers at the OLCF a
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.
91°µÍø 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.