Machine Learning Prediction and Classification of Transmission Functions for Rapid DNA Sequencing in a Hybrid Nanopore

Electrical DNA sequencing using solid-state nanopores has emerged as a promising technology due to its potential to achieve high-precision single-base resolution. However, uncontrollable nucleotide translocation, low signal-to-noise ratios, and electrical signal overlapping from nucleotide stochastic motion have been major limitations. Recent fabrication of in-plane hybrid heterostructures of 2D materials has triggered active research in sequencing applications due to their interesting electrical properties. Herein, our study explores both machine learning (ML) regression and a classification framework for single DNA nucleotide identification with hybrid graphene/hexagonal boron nitride (G/h-BN) nanopores using a quantum transport approach. The optimized ML model predicted each nucleotide at its most stable configuration with the lowest root-mean-squared error of 0.07. We have also examined the impact of three locally polarized hybrid nanopore environments (C delta--H delta+, N delta--H delta+, and B delta+-H delta-) on ML prediction of transmission functions utilizing structural, chemical, and electrical environmental descriptors. The random forest algorithm demonstrates notable classification accuracy across quaternary (similar to 86%), ternary (similar to 95%), and binary (similar to 98%) combinations of four nucleotides. Further, we checked the applicability of the hybrid nanopore device with conductance sensitivity and Frontier molecular orbital analysis. Our study showcases the potential of a hybrid nanopore with the ML-combined quantum transport method as a promising sequencing platform that paves the way for advancements in solid-state nanopore sequencing technologies.