  The formation, properties and lifetime of secondary organic aerosols in the
atmosphere are largely determined by gas-particle partitioning coefficients of
the participating organic vapours. Since these coefficients are often difficult
to measure or compute, we developed a machine learning (ML) model to predict
them given molecular structure as input. Our data-driven approach is based on
the dataset by Wang et al. (Atmos. Chem. Phys., 17, 7529 (2017)), who computed
the partitioning coefficients and saturation vapour pressures of 3414
atmospheric oxidation products from the master chemical mechanism using the
COSMOtherm program. We train a kernel ridge regression (KRR) ML model on the
saturation vapour pressure ($P_{sat}$), and on two equilibrium partitioning
coefficients: between a water-insoluble organic matter phase and the gas phase
($K_{WIOM/G}$), and between an infinitely dilute solution with pure water and
the gas phase ($K_{W/G}$). For the input representation of the atomic structure
of each organic molecule to the machine, we test different descriptors. Our
best ML model predicts $P_{sat}$ and $K_{WIOM/G}$ to within 0.3 and $K_{W/G}$
to within 0.4 logarithmic units of the original COSMOtherm calculations. This
is equal or better than the typical accuracy of COSMOtherm predictions compared
to experimental data. We then apply our ML model to a dataset of 35,383
molecules that we generated based on a carbon 10 backbone and functionalized
with 0 to 6 carboxyl, carbonyl or hydroxyl groups to evaluate its performance
for polyfunctional compounds with potentially low $P_{sat}$. The resulting
$P_{sat}$ and partitioning coefficient distributions were physico-chemically
reasonable, and the volatility predictions for the most highly oxidized
compounds were in qualitative agreement with experimentally inferred
volatilities of atmospheric oxidation products with similar elemental
composition.
