  Structural manipulation at the nanoscale breaks the intrinsic correlations
among different energy carrier transport properties, achieving high
thermoelectric performance. However, the coupled multifunctional (phonon and
electron) transport in the design of nanomaterials makes the optimization of
thermoelectric properties challenging. Machine learning brings convenience to
the design of nanostructures with large degree of freedom. Herein, we conducted
comprehensive thermoelectric optimization of isotopic armchair graphene
nanoribbons (AGNRs) with antidots and interfaces by combining Green's function
approach with machine learning algorithms. The optimal AGNR with ZT of 0.894 by
manipulating antidots was obtained at the interfaces of the aperiodic isotope
superlattices, which is 5.69 times larger than that of the pristine structure.
The proposed optimal structure via machine learning provides physical insights
that the carbon-13 atoms tend to form a continuous interface barrier
perpendicular to the carrier transport direction to suppress the propagation of
phonons through isotope AGNRs. The antidot effect is more effective than
isotope substitution in improving the thermoelectric properties of AGNRs. The
proposed approach coupling energy carrier transport property analysis with
machine learning algorithms offers highly efficient guidance on enhancing the
thermoelectric properties of low-dimensional nanomaterials, as well as to
explore and gain non-intuitive physical insights.
