When examining the most popular domain-agnostic machine learning methods that are applied to the materials domain (Figure ), traditional tree-based machine learning techniques are found to be the most dominant. The top two cited works are related to treebased methods (random forest and XGBoost ), and the third (Gradient Boosting ) is also frequently applied to tree-based methods. The use of deep learning is becoming more popular; nevertheless, the larger data sets needed to train these algorithms likely inhibits more widespread usage. Finally, methods for interpreting machine learning methods, including SHAP model explanations and t-SNE , are also popular in the materials community.