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The integration of machine learning techniques in materials discovery hasbecome prominent in materials science research and has been accompanied by anincreasing trend towards open-source data and tools to propel the field.Despite the increasing usefulness and capabilities of these tools, developersneglecting to follow reproducible practices creates a significant barrier forresearchers looking to use or build upon their work. In this study, weinvestigate the challenges encountered while attempting to reproduce a sectionof the results presented in "A general-purpose machine learning framework forpredicting properties of inorganic materials." Our analysis identifies fourmajor categories of challenges: (1) reporting computational dependencies, (2)recording and sharing version logs, (3) sequential code organization, and (4)clarifying code references within the manuscript. The result is a proposed setof tangible action items for those aiming to make code accessible to, anduseful for the community.

Reproducibility in Computational Materials Science: Lessons from 'A  General-Purpose Machine Learning Framework for Predicting Properties of  Inorganic Materials'