  The formation of ice in the atmosphere affects precipitation and cloud
properties, and plays a key role in the climate of our planet. Although ice can
form directly from liquid water at deeply supercooled conditions, the presence
of foreign particles can aid ice formation at much warmer temperatures. Over
the past decade, experiments have highlighted the remarkable efficiency of
feldspar minerals as ice nuclei compared to other particles present in the
atmosphere. However, the exact mechanism of ice formation on feldspar surfaces
has yet to be fully understood. Here, we develop a first-principles
machine-learning model for the potential energy surface aimed at studying ice
nucleation at microcline feldspar surfaces. The model is able to reproduce with
high fidelity the energies and forces derived from density-functional theory
(DFT) based on the SCAN exchange and correlation functional. We apply the
machine-learning force field to study different fully-hydroxylated terminations
of the (100), (010), and (001) surfaces of microcline exposed to vacuum. Our
calculations suggest that terminations that do not minimize the number of
broken bonds are preferred in vacuum. We also study the structure of
supercooled liquid water in contact with microcline surfaces, and find that
water density correlations extend up to around 1 nm from the surfaces. Finally,
we show that the force field maintains a high accuracy during the simulation of
ice formation at microcline surfaces, even for large systems of around 30,000
atoms. Future work will be directed towards the calculation of nucleation free
energy barriers and rates using the force field developed herein, and
understanding the role of different microcline surfaces on ice nucleation.
