Simultaneous Prediction and Optimization of Charge Transfer Properties of Graphene and Graphene Oxide Nanoflakes from Multitarget Machine Learning

Considerable effort is directed toward controlling the physicochemical structure of graphene and graphene oxide, but complex structure/property relationships are difficult to identify and utilize when the materials are multifunctional and the properties are correlated. In this study, we propose and demonstrate a workflow for predicting which structural features to use to tune correlated properties simultaneously. Highly accurate multitarget regressors predict the ionization potential and electron affinity of graphene and graphene oxide nanoflakes and report the most important structural features as a basis for ensemble filtering that reflects design decisions. To challenge the approach, multiobjective optimization was used to find filters that simultaneously lower the ionization potential by −0.5 eV and raise the electron affinity by 0.5 eV. We find that the diameter of graphene nanoflakes is the most useful structural feature of graphene but is superseded by the oxygen concentration and proximity to the edges in graphene oxide. Achieving our challenging design goal was not possible, but a significant and balanced shift in the properties (in the right directions) could be obtained and accompanied by improved quality and performance. This general approach could be used to predict filters and to guide experimental design to separate samples for specific applications.