Researchers from Oak Ridge National Laboratory (ORNL) used high-throughput computational techniques to identify a new class of 2D nanomaterial, MXenes including boron-nitride.
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A collaborative team of researchers from Oak Ridge National Laboratory (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 me
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
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.
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.
Transformer language models provide state-of-the-art accuracy in a range of learning tasks, ranging from natural language processing to non-traditional applications such as molecular design.
Oak Ridge National Laboratory researchers developed an invertible neural network (INN) to effectively and efficiently solve earth-system model calibration and simulation problems.