A multidisciplinary team of researchers from Oak Ridge National Laboratory (ORNL) and other institutions created a Machine Learning (ML) library for the training of classifiers on spectrographic chemical data.
A multidisciplinary team of researchers from Oak Ridge National Laboratory (ORNL) pioneered the use of the LLVM-based high-productivity/high-performance Julia language unifying capabilities to write an end-to-end workflow on Frontier, the first US Depar
A team of researchers from Oak Ridge National Laboratory (ORNL) released the initial draft of the Interconnected Science Ecosystem (INTERSECT) architecture specification.
We developed a novel uncertainty-aware framework MatPhase to predict material phases of electrodes from low contrast SEM images.
We released two open-source datasets named GDB-9-Ex and ORNL_AISD-Ex that provide calculations of electronic excitation energies and their associated oscillator strengths based on the time-dependent density-functional tight-binding (TD-DFTB) method.
A team of researchers from the Oak Ridge National Laboratory (ORNL) developed a novel architecture for a hybrid quantum-classical neural network.
Researchers from Oak Ridge National Laboratory (ORNL), in collaboration with researchers from Duke University, have developed an unsupervised machine learning method, NashAE, for effective disentanglement of latent representations.