A multidisciplinary ORNL team used expertise in synthetic biology, AI-driven analysis, chemistry, neutrons and materials science to identify new members of a family of enzymes with a natural affinity for degrading synthetic nylon polymers.
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.
Researchers from Oak Ridge National Laboratory and the University of Central Florida have extended an evolutionary approach for training spiking neural networks.
The researchers from ORNL have developed a new and faster algorithm for the graph all-pair shortest-path (APSP) problem.
A team of researchers from Oak Ridge National Laboratory applied advanced statistical methods from biomedical research to study an unexpected failure mode of general-purpose computing on graphics processing units (GPGPUs).
Metal halide perovskites are promising materials for optoelectronic and sensing applications.
Researchers developed a novel algorithm for resilient and communication-efficient parallel matrix multiplication in HPC systems.
Researchers built a deep neural network to estimate the compressibility of scientific data.
To help expedite the use of quantum processing units, ORNL researchers developed an advanced software framework.
A team of ORNL researchers has used the DCA++ application, a popular code for predicting the performance of quantum materials, to verify two performance-enhancing strategies.