  With the advent of powerful computer simulation techniques, it is time to
move from the widely used knowledge-guided empirical methods to approaches
driven by data science, mainly machine learning algorithms. We investigated the
predictive performance of three machine learning algorithms for six different
glass properties. For such, we used an extensive dataset of about 150,000 oxide
glasses, which was segmented into smaller datasets for each property
investigated. Using the decision tree induction, k-nearest neighbors, and
random forest algorithms, selected from a previous study of six algorithms, we
induced predictive models for glass transition temperature, liquidus
temperature, elastic modulus, thermal expansion coefficient, refractive index,
and Abbe number. Moreover, each model was induced with default and tuned
hyperparameter values. We demonstrate that, apart from the elastic modulus
(which had the smallest training dataset), the induced predictive models for
the other five properties yield a comparable uncertainty to the usual data
spread. However, for glasses with extremely low or high values of these
properties, the prediction uncertainty is significantly higher. Finally, as
expected, glasses containing chemical elements that are poorly represented in
the training set yielded higher prediction errors. The method developed here
calls attention to the success and possible pitfalls of machine learning
algorithms. The analysis of the SHAP values indicated the key elements that
increase or decrease the value of the modeled properties. It also estimated the
maximum possible increase or decrease. Insights gained by this analysis can
help empirical compositional tuning and computer-aided inverse design of glass
formulations.
