posted on 2020-03-18, 18:08authored byBenyamin Motevalli, Baichuan Sun, Amanda S. Barnard
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.