  Determining, understanding, and predicting the so-called structure-property
relation is an important task in many scientific disciplines, such as
chemistry, biology, meteorology, physics, engineering, and materials science.
Structure refers to the spatial distribution of, e.g., substances, material, or
matter in general, while property is a resulting characteristic that usually
depends in a non-trivial way on spatial details of the structure.
Traditionally, forward simulations models have been used for such tasks.
Recently, several machine learning algorithms have been applied in these
scientific fields to enhance and accelerate simulation models or as surrogate
models. In this work, we develop and investigate the applications of six
machine learning techniques based on two different datasets from the domain of
materials science: data from a two-dimensional Ising model for predicting the
formation of magnetic domains and data representing the evolution of dual-phase
microstructures from the Cahn-Hilliard model. We analyze the accuracy and
robustness of all models and elucidate the reasons for the differences in their
performances. The impact of including domain knowledge through tailored
features is studied, and general recommendations based on the availability and
quality of training data are derived from this.
