We developed a novel uncertainty-aware framework MatPhase to predict material phases of electrodes from low contrast SEM images.
We released two open-source datasets named GDB-9-Ex and ORNL_AISD-Ex that provide calculations of electronic excitation energies and their associated oscillator strengths based on the time-dependent density-functional tight-binding (TD-DFTB) method.
A multidisciplinary team of researchers from 91做厙 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.
