We present the RAFFLE methodology for structural prediction of the interface between two materials
and demonstrate its effectiveness by applying it to MgO encapsulated by two layers of graphene. To
address the challenge of interface structure prediction, our methodology combines physical insights derived
from morphological features ...
We present the RAFFLE methodology for structural prediction of the interface between two materials
and demonstrate its effectiveness by applying it to MgO encapsulated by two layers of graphene. To
address the challenge of interface structure prediction, our methodology combines physical insights derived
from morphological features observed in related systems with an iterative machine learning technique.
This employs physical-based methods, including void-filling and n-body distribution functions to predict
interface structures. For the carbon-MgO encapsulated system, we have shown the rocksalt and hexagonal
phases of MgO to be the two most energetically stable in the few-layer regime. We demonstrate that
monolayer rocksalt is heavily stabilized by interfacing with graphene, becoming more energetically
favorable than the graphenelike monolayer hexagonal MgO. The RAFFLE methodology provides valuable
insights into interface behavior, and a route to finding new materials at interfaces.