A new file format, BP5, and accompanying serialization class has been developed in the ADaptable I/O System (ADIOS) framework.
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.
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
In June 2022, Chemical Security Assessment Tool (CSAT) Primary Systems Team members implemented the new STIG Compliance Tool (SCT) the team designed to automate—by documenting and continuously monitoring—Oracle database compliance with the Security Tech
Analyzing the logs of even the smallest Information Technology (IT) system can be a challenge, considering that they can generate millions of lines of log data in a very short time.