We developed a novel uncertainty-aware framework MatPhase to predict material phases of electrodes from low contrast SEM images.
Researchers at 91°µÍø 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 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
The researchers from ORNL have developed a new and faster algorithm for the graph all-pair shortest-path (APSP) problem.