Graph Neural Network Guided Selection of Functional Polymers for Charge Transfer Doping of 2D Materials

Abstract

Two-dimensional (2D) semiconductors are competing materials for post-Si transistors because of their desirable carrier mobilities and band gaps with atomic thinness. However, high contact resistances plague the performance of 2D-based devices. Thus, non-destructive doping strategies are needed in order to overcome this challenge for complementary (n-type and p-type) 2D logic. Here, we present the use of functional polymers to non-destructively introduce charge transfer n-type and p-type doping in 2D materials. First, we utilize a multitask graph neural network (GNN), trained on density functional theory–calculated properties, to predict properties like ionization energy and electron affinity of candidate polymers. These predictions are then used to select polymers capable of doping n-type and p-type monolayer MoS₂ and WSe₂ based on band-level alignment criteria. We validate our screening strategy with Raman spectroscopy, confirming successful p-type doping of WSe₂ with PEDOT:PSS and Nafion, and n-type doping of MoS₂ with PEI and WSe₂ with p(gPyDPP-MeOT2). The results demonstrate a practical model-to-lab pipeline that effectively narrows the search space for polymer dopants, bridging high-throughput computational materials screening with experimental validation.