Understanding and Predicting the Cause of Defects in Graphene Oxide Nanostructures Using Machine Learning

Abstract

Machine learning is a powerful way of uncovering hidden structure/property relationships in nanoscale materials, and it is tempting to assign structural causes to properties based on feature rankings reported by interpretable models. In this study of defective graphene oxide nanoflakes, we use classification, regression, and causal inference to show that not all important structural features directly influence the concentration of broken bonds, as a representative property. We find that while the presence of oxygen is important for actual bond breakage the presence and distribution of hydrogen determines how often bond breakage occurs.