However, as ML transitions from a novel approach to a common practice for data analysis in the sciences, the accurate curation, training, deployment, and final reporting of data analysis outcomes should be emphasized. To aid this effort, we have compiled perspectives and lessons learned from the fields of data science and materials science for emerging ML and high-throughput studies. the training and testing datasets resulting in overoptimistic models 128 . For example, Vecsei et al. and Park et al. deleted duplicate entries in the source ICSD domain, and Suzuki et al. proposed a method to separate testing and training datasets chronologically to reduce the chance of leakage into the testing dataset.