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.
Evaluate the historical performance and future projections of compound heatwave and drought (CHD) extremes across the contiguous United States using CMIP6 global climate models, providing insights for regional adaptation strategies in response to
The objective of this study is to explore and analyze the spatial patterning of sociodemographic disparities in extreme heat exposure across multiple scales within the Conterminous United States (CONUS).
ORNL researchers developed a novel nonlinear level set learning method to reduce dimensionality in high-dimensional function approximation.
Researchers at ORNL have created a unique simulation technology that allows software systems to participate in slower than real time simulation exercises, and to accomplish this without requiring recompilation of source code, relinking of object files,
The team conducted numerical studies to demonstrate the connection between the parameters of neural networks and the stochastic stability of DMMs.
Researchers from Oak Ridge National Laboratory (ORNL) demonstrated that mode connectivity exists in the loss landscape of parameterized quantum circuits.
Metal Halide Perovskites (MHPs) offer promise for applications in PVs and LEDs due to high device performance and low fabrication cost.
Domain dynamics in polycrystalline materials are explored using a workflow combining deep learning-based segmentation of domain structures with non-linear dimensionality reduction using multilayer rotationally invariant autoencoders (rVAE).