News

May 7, 2025

6 MIN READ

AI Initiative projects receive NAIRR, INCITE awards for foundation models

Research teams at the Department of Energy’s Oak Ridge National Laboratory received computing resource awards to train and test AI foundation models for science. A total of six ORNL projects were awarded allocations from the National Artificial Intelligence Research Resource, or NAIRR, pilot and the Innovative and Novel Computational Impact on Theory and Experiment, or INCITE, program to train their AI models.

January 18, 2024

5 MIN READ

Researchers at ORNL demonstrate scalability of GNNs on world’s most powerful computing systems

Researchers at the Department of Energy’s Oak Ridge and Lawrence Berkeley National Laboratories are evolving graph neural networks to scale on the nation’s most powerful computational resources, a necessary step in tackling today’s data-centric

July 18, 2022

4 MIN READ

Scalable Training of Graph Convolutional Neural Networks for Fast and Accurate Predictions of Molecular Photo-Optical Properties

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 and NERSC supercomputing centers, respectively.

February 14, 2022

2 MIN READ

HydraGNN – Distributed PyTorch implementation of multi-headed graph convolutional neural networks

Researchers from the Computing and Computational Sciences Directorate (CCSD) at Oak Ridge National Laboratory (ORNL) have developed a distributed implementation of graph convolutional neural networks [1]. The code has been shown to successfully take advantage of HPC resources on ORNL-CADES-DGX, ORNL-CADES-CONDO, and ORNL-OLCF-SUMMIT to produce fast and accurate predictions of macroscopic material properties using atomic information.

December 6, 2021

3 MIN READ

A Prediction Interval Method for Machine Learning Model Uncertainty Quantification

October 18, 2021

5 MIN READ

HydraGNN – Distributed PyTorch Implementation of Multi-Headed Graph Convolutional Neural Networks