We developed a novel uncertainty-aware framework MatPhase to predict material phases of electrodes from low contrast SEM images.
A multidisciplinary team of researchers from Oak Ridge National Laboratory and the University of Texas at Austin developed a new machine-learning-based reduced-order model called GrainNN to predict the grain structure that forms as a metal solidifies.
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
A multi-institutional team of ORNL has utilized the latest computational algorithms and parallelization techniques to enable faster than real-time simulations and applied it to the power system network whose time-domain model represents very large and h
ORNL researchers developed a stochastic approximate gradient ascent method to reduce posterior uncertainty in Bayesian experimental design involving implicit models.
Dendritic solidification and microstructure evolution play a vital role in determining the material properties. Capturing the morphology of the solidification front becomes critical in predicting the final dendritic structure.