  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.
