  Aerosol particles play an important role in the climate system by absorbing
and scattering radiation and influencing cloud properties. They are also one of
the biggest sources of uncertainty for climate modeling. Many climate models do
not include aerosols in sufficient detail. In order to achieve higher accuracy,
aerosol microphysical properties and processes have to be accounted for. This
is done in the ECHAM-HAM global climate aerosol model using the M7 microphysics
model, but increased computational costs make it very expensive to run at
higher resolutions or for a longer time. We aim to use machine learning to
approximate the microphysics model at sufficient accuracy and reduce the
computational cost by being fast at inference time. The original M7 model is
used to generate data of input-output pairs to train a neural network on it. By
using a special logarithmic transform we are able to learn the variables
tendencies achieving an average $R^2$ score of $89\%$. On a GPU we achieve a
speed-up of 120 compared to the original model.
