Effect of graphene electrode functionalization on machine learning-aided single nucleotide classification.

Abstract

Solid-state nanogap-based DNA sequencing with a quantum tunneling approach has emerged as a promising avenue due to its potential to deliver swift and precise sequencing outcomes. Nevertheless, despite significant progress, experimentally achieving single base resolution with a high signal-to-noise ratio remains a daunting challenge. In this work, we have utilized a machine learning (ML) framework coupled with the quantum transport method to assess and compare the nucleotide identification performance of graphene nanogaps functionalized with four different edge-saturating entities (C, H, N, and OH). The optimized ML model, especially the random forest classifier (RFC), demonstrates high accuracy (>90%) in classifying unlabeled nucleotides from their transmission readouts with the four functionalized graphene nanogaps. Additionally, the minor variance in the accuracy of nucleotide classification across the nanogaps highlights that RFC can capture the role of electrode-nucleotide coupling dynamics in their transmission function. Moreover, we have also conducted conductance sensitivity (%) and current-voltage (I-V) analyses of each functionalized nanogap. Among the edge-saturating entities, the nitrogen atom terminated graphene nanogap (NGN) is found to be the most sensitive for distinguishing DNA nucleotides. Our quantum transport combined ML study provides a useful perspective by conducting a comparative analysis of the role of edge-saturating entities in single-molecule DNA sequencing.