  Machine learning (ML) is increasingly used in the field of materials science,
where statistical estimates of computed properties are employed to rapidly
examine the chemical space for new compounds. However, a systematic comparison
of several ML models for this domain has been hindered by the scarcity of
appropriate datasets of materials properties, as well as the lack of thorough
benchmarking studies. To address this, a public data-analytics competition was
organized by the Novel Materials Discovery (NOMAD) Centre of Excellence and
hosted by the on-line platform Kaggle using a dataset of $3\,000$ (Al$_x$
Ga$_y$ In$_z$)$_2$ O$_3$ compounds (with $x+y+z = 1$). The aim of this
challenge was to identify the best ML model for the prediction of two key
physical properties that are relevant for optoelectronic applications: the
electronic band gap energy and the crystalline formation energy. In this
contribution, we present a summary of the top three ML approaches of the
competition including the 1st place solution based on a crystal graph
representation that is new for ML of the properties of materials. The 2nd place
model combined many candidate descriptors from a set of compositional, atomic
environment-based, and average structural properties with the light
gradient-boosting machine regression model. The 3rd place model employed the
smooth overlap of atomic positions representation with a neural network. To
gain insight into whether the representation or the regression model determines
the overall model performance, nine ML models obtained by combining the
representations and regression models of the top three approaches were compared
by looking at the correlations among prediction errors. At fixed
representation, the largest correlation is observed in predictions made with
kernel ridge regression and neural network, reflecting a similar performance on
the same test set samples.

Materials Science