  We present work flows and a software module for machine learning model
building in surface science and heterogeneous catalysis. This includes
fingerprinting atomic structures from 3D structure and/or connectivity
information, it includes descriptor selection methods and benchmarks, and it
includes active learning frameworks for atomic structure optimization,
acceleration of screening studies and for exploration of the structure space of
nano particles, which are all atomic structure problems relevant for surface
science and heterogeneous catalysis. Our overall goal is to provide a
repository to ease machine learning model building for catalysis, to advance
the models beyond the chemical intuition of the user and to increase autonomy
for exploration of chemical space.
