Efficient high-throughput method utilizing neural network potentials to calculate interaction energies, validated by clean transfer experiment of CVD graphene with polymer mixtures

Abstract

In this study, we designed a computational model involving energy decomposition using ground state energy minimized geometries resulting from a general-purpose neural network potential (ANI-1ccx). The numerical simulations show a distribution of energies, which indicate a two-fold reduction in interaction energy and polarity shift in electrostatic interaction, highlighting the computational novelty in exploring over a million metastable configurations. Experimentally, we validate our model by observing that using a mixture of two distinct polymers in the wet transfer process reduces transfer-induced doping and strain on transferred CVD graphene compared to the conventional single polymer wet transfer method, primarily due to decreased polymer contamination from the transfer process. This reduction is linked to the decreased interaction energy in the mixture of polymethyl methacrylate and angelica lactone polymer on graphene.