# Machine Learning Prediction of DNA Charge Transport

## TL;DR

This paper introduces a machine learning model that efficiently predicts the electrical conductance of long DNA sequences, significantly reducing computational costs while accurately capturing various charge transport mechanisms.

## Contribution

The study presents a novel ML approach trained on short DNA segments to predict conductance in long sequences, enabling rapid screening of DNA-based conductors.

## Key findings

- ML accurately predicts conductance across different transport mechanisms
- Clusters of nucleotides influence DNA charge transport
- Method reduces computational costs by orders of magnitude

## Abstract

First principle calculations of charge transfer in DNA molecules are computationally expensive given that charge carriers migrate in interaction with intra- and inter-molecular atomic motion. Screening sequences, e.g. to identify excellent electrical conductors is challenging even when adopting coarse-grained models and effective computational schemes that do not explicitly describe atomic dynamics. In this work, we present a machine learning (ML) model that allows the inexpensive prediction of the electrical conductance of millions of {\it long} double-stranded DNA (dsDNA) sequences, reducing computational costs by orders of magnitude. The algorithm is trained on {\it short} DNA nanojunctions with $n=3-7$ base pairs. The electrical conductance of the training set is computed with a quantum scattering method, which captures charge-nuclei scattering processes. We demonstrate that the ML method accurately predicts the electrical conductance of varied dsDNA junctions tracing different transport mechanisms: coherent (short-range) quantum tunneling, on-resonance (ballistic) transport, and incoherent site-to-site hopping. Furthermore, the ML approach supports physical observations that clusters of nucleotides regulate DNA transport behavior. The input features tested in this work could be used in other ML studies of charge transport in complex polymers, in the search for promising electronic and thermoelectric materials.