  Establishing the structure-property relationship for grain boundaries (GBs)
is critical for developing next generation functional materials, but has been
severely hampered due to its extremely large configurational space. Atomistic
simulations with low computational cost and high predictive power are strongly
desirable, but the conventional simulations using empirical interatomic
potentials and density functional theory suffer from the lack of predictive
power and high computational cost, respectively. A machine learning interatomic
potential (MLIP) recently emerged but often requires an extensive size of the
training dataset, making it a less feasible approach. Here we demonstrate that
an MLIP trained with a rationally designed small training dataset can predict
thermal transport across GBs in graphene with ab initio accuracy at an
affordable computational cost. In particular, we employed a rational approach
based on the structural unit model to find a small set of GBs that can
represent the entire configurational space and thus can serve as a
cost-effective training dataset for the MLIP. Only 5 GBs were found to be
enough to represent the entire configurational space of graphene GBs. Using the
atomistic Green's function approach and the MLIP, we revealed that the
structure-thermal resistance relation in graphene does not follow the common
understanding that large dislocation density causes larger thermal resistance.
In fact, thermal resistance is nearly independent of dislocation density at
room temperature and is higher when the dislocation density is small at
sub-room temperature. We explain this intriguing behavior with the buckling
near a GB causing a strong scattering of flexural phonon modes.
