Machine learning, being a novel method to materials researchers, required some standardization and guidelines to help authors presenting their research in the most effective way. Wang et al. provided a guideline on how to apply ML for material property prediction. The paper provides a blueprint for data acquisition, featurization techniques, model generation, testing, and parameter optimization. Following the Machine Learning Guidelines for Materials Scientists, we would like to share with the community the most recent list of properties that we use for ML feature generation based on composition, providing the researchers the data we used in over a dozen of experimentally validated ML prediction studies.