Charge-dependent Fermi level of graphene oxide nanoflakes from machine learning

Although the energy of the Fermi level is of critical importance to designing electrically conductive materials, heterostructures and devices, the relationship between the Fermi energy and complex structure of graphene oxide has been difficult to predict due to competing dependencies on oxygen concentration and distribution, defects and charge. In this study we have used a data set of over 60,000 unique graphene oxide nanostructures and interpretable machine learning methods to show that the principal determinant is the ionic charge, which is in itself structure-independent. From this we define three separate, highly accurate, charge-dependent structure/property relationships and show that the Fermi energy can be predicted based on the ether concentration, hydrogen passivation or size, for the neutral, anionic and cationic cases, respectively. These important features can inform experimental design, and are remarkably insensitive to minor structural variations that are difficult to control in the lab.