  In this study, we investigate both experimentally and computationally the
molecular interactions of two distinct polymers with graphene. Our experimental
findings indicate that the use of a polymer mixture reduces the transfer
induced doping and strain in fabricated graphene devices as compared to
conventional single polymer wet transfer. We found that such reduction is
related to the decreased affinity of mixture of polymethyl methacrylate and
angelica lactone polymer for graphene. We investigated changes in binding
energy (BE) of polymer mixture and graphene by considering energy decomposition
analysis using a pre-trained potential neural network. It was found that
numerical simulations accurately predicted two-fold reduction of BE and order
of magnitude reduction of electrostatic interaction between polymers.
