The rapid rise in publications on the topic of machine learning in materials science is fueled by advancements in software, material databases, domain-specific materials science methods, and domain-agnostic machine learning methods (Figure ). To examine this relationship further beyond a simple publication count increase, I compiled a data set of 6795 research papers on the topic of materials machine learning and subsequently analyzed the citations for each of these papers using the Scopus API and pybliometrics Python library. I then analyzed the data set to determine all papers that were cited at least 100 times within materials science machine learning papers to determine commonly used tools and techniques within this subdomain. We note that this method only counts citations within the set of 6795 materials machine learning papers, and therefore is lower than a full citation count which may include citations from many domains or study types. First, I focust on the results for software (Figure ). The most commonly used software and the mostly highly cited work within the data set overall is the scikit-learn Python package. This may stem from several reasons. First, scikit-learn implements a variety of techniques that can be useful for many different types of materials machine learning. For example, it can be applied to predict the band gaps of solids , to predict the strength of cement composites , to associate processing conditions with final properties in batteries , to predict the fatigue life of powder metallurgy components , or for many other materials tasks. Furthermore, although scikit-learn is missing the capability to implement more complex deep learning models, the small data set sizes of many materials problems often make it practical to use more conventional machine learning algorithms that have fewer parameters to train. Overall, it is interesting to note that the most commonly cited paper within the entire data set is a general-purpose machine learning tool rather than a domainspecific tool.