A multidisciplinary team of researchers has developed an adaptive physics refinement (APR) technique to effectively model cancer cell transport.
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A multi-university team first reported a unique lead-free ferroelectric compound - (Ca,Sr)3Mn2O7, which belongs to a class of materials described as hybrid improper ferroelectrics.
A web-based GUI for INTERSECT has been created which allows a user to configure an experiment on an electron microscope, setting such parameters as maximum number of steps for the machine learning algorithm to perform.
We present an intercomparison of a suite of high-resolution downscaled climate projections based on a six-member General Climate Models (GCM) ensemble from the 6th Phase of Coupled Models Intercomparison Project (CMIP6).
Researchers at Oak Ridge National Laboratory developed a new parallel performance portable algorithm for solving the Euclidean minimum spanning tree problem (EMST), capable of processing tens of millions of data points a second.
A team of Oak Ridge National Laboratory (ORNL) scientists involved in research topics of cybersecurity, statistical approaches, control systems, and dynamical models, reported a basic approach to security of physical systems that are interfaced with IT
Researchers associated with the ExaAM project, a part of the Exascale Computing Project, developed ExaCA, a cellular automata (CA)-based model for grain-scale alloy solidification capable of simulation on both CPU and GPU architectures.
Researchers associated with the ExaAM project, a part of the Exascale Computing Project, developed ExaCA, a cellular automata (CA)-based model for grain-scale alloy solidification capable of simulation on both CPU and GPU architectures.
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