Using in-situ atomic resolution HAADF-STEM imaging, the study provides direct experimental evidence that grain rotation in nanocrystalline materials is primarily driven by disconnection-mediated shear-coupled grain boundary (GB) migration, confirming a...

We introduce a neural network kinetics (NNK) scheme that predicts and simulates diffusion-induced chemical and structural evolution in complex concentrated chemical environments. The framework is grounded on efficient on-lattice structure and chemistry representation...

Machine learning (ML) models can be interpretable, providing new fundamental physical/chemical insights into materials systems. We developed interpretable ML models1 to investigate two very different materials systems: • Peptide “wires” experimentally shown to exhibit...