On-the-fly digital twins of materials evolution using autonomous computational workflows at the Exascale

Traditional approaches to bridge atomistic dynamics with experimental observations at the microstructural level often rely on phenomenological models of the underlying physics, whose free parameters are in turn fitted to a small number of intuition-driven atomic scale simulations under limited number of thermodynamical drivers (e.g., temperature, pressure, chemical potential etc). This tedious and time-consuming approach becomes particularly cumbersome to study synthesis and characterization of materials with complex dependencies on local environment, temperature and lattice-strains e.g., heterostructure interfaces of nanomaterials. In this talk, I will present workflows that couple automated exascale high-throughput large-scale molecular dynamics simulations with a wide range of Bayesian uncertainty quantification-driven active learning paradigms for on-the-fly learning of material synthesis. By implementing such a workflow to study recrystallization of amorphous transition-metal dichalcogenide (TMDC) phases under various growth parameters, I will show that such automated scale-bridging frameworks can be promising towards achieving controlled epitaxy of targeted multilayer moiré devices paving the way towards a robust autonomous discovery pipeline to enable unprecedented functionalities.