Quantum-simulation-informed machine learning of dynamic properties of two-dimensional and layered materials

Two-dimensional and layered transitional metal dichalcogenides are emerging as promising materials for the electronic and optoelectronic devices of tomorrow due to the large space of design variables (such as configuration of dopant atoms, sequence of stacking along the van der Waals direction etc.) that can be used to tune dynamic properties of the material. The primary challenge for rational design of these materials is navigating this complex design space to identify optimal structures and compositions that possess desired properties. In this work, we show that machine-learning methods applied to atomistic data from quantum mechanical simulations are highly suitable for predicting optimal structures with respect to dynamic properties like thermal, charge and spin transport, electron-phonon coupling and non-equilibrium phonon distributions, and propensity for structural and phase transformations.