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...

The realization of chiral and non-reciprocal excitations in inorganic crystalline materials necessitates the development of new atomically-precise chiral solid state crystals and understand their fundamental optical/electronic states that arise from the resulting...