ChemML is an open machine learning (ML) and informatics program suite that is designed to support and advance the data‐driven research paradigm that is currently emerging in the chemical and materials domain. ChemML allows its users to perform various data science tasks and execute ML workflows that are adapted specifically for the chemical and materials context. Key features are automation, general‐purpose utility, versatility, and user‐friendliness in order to make the application of modern data science a viable and widely accessible proposition in the broader chemistry and materials community. ChemML is also designed to facilitate methodological innovation, and it is one of the cornerstones of the software ecosystem for data‐driven in silico research.

Provide a case study to understand better different stages

• We make an effort to reach non-expert users for which the novelty of ML research may be daunting.

• Technical details (i.e., tricks of the trade) are as important as the core elements of any ML workflow. Thus, we want to share best practices and guidelines to guarantee the quality and reproducibility of data-driven studies.