Abstract: Ab Initio and Machine Learning Modelling of Twisted Bilayers and Heterostructures of 2D Materials
2D Materials exhibit a wide range of novel physics, much of it driven by behaviour such as flat bands and van Hove singularities emerging when layered materials are combined as twisted multi-layer systems or heterostructures. However, predicting the electronic and vibrational properties of 2D materials systems whose moiré supercells are large is challenging for conventional modelling approaches. I present work using two complementary approaches to large scale simulation of 2D Materials. Firstly, Linear-Scaling DFT, using the ONETEP code [1], allows us to calculate electronic properties as well as energies and forces of moiré-scale systems; secondly, Machine-Learned Interatomic Potentials using equivariant neural networks such as MACE [2] can match DFT accuracy, enabling geometry optimisation, molecular dynamics and spectroscopy at the same scale. I will discuss applications of these combined methods including calculating domain reconstruction, predicted Raman spectra and unfolded electronic band structure for twisted TMD sand alloy TMDs, and for heterostructures involving TMDs, hBN and graphene.
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