<scp>MRMinerals</scp> and <scp>MineralTD</scp> : Machine‐Readable Mineral Formula and Compositions Data Set for Data‐Driven Research

ABSTRACT Artificial intelligence (AI) is being increasingly applied in the geosciences, particularly in fields like mineralogy, where it supports tasks such as mineral classification, automated thin‐section image analysis, or mineral exploration targeting. Such tasks require large structured and standardized data sets, which are currently not available. We build two databases to fill this gap: (i) MRMinerals contains a list of the 400 most common and geologically significant minerals, including major rock‐forming minerals, key accessory minerals, and economically important ore minerals with machine‐readable formulas as the key feature. (ii) MineralTD contains a large training data set with 10,000+ compositions for each of the 400 minerals in MRMinerals. MineralTD is split into two subdatasets: MineralTDMeasured and MineralTDSynthetic . MineralTDMeasured contains approximately 140,000 mineral compositions from the open‐access geochemical databases and repositories GEOROC, Pangaea, PetDB, RRUFF, and ESMD. MineralTDSynthetic contains synthetic mineral compositions, generated using machine‐readable formulas from MRMinerals, with at least 10,000 compositions per mineral. MineralTD is annotated with metadata, such as mineral frequency, rock classification, data source, and methods used to provide a full understanding of the individual data set. The MRMinerals and MineralTD are ready‐to‐use open access data sets that enable scalable, data‐driven research in mineralogy, e.g., ML applications.