MRMinerals and MineralTD: Machine-Readable Mineral Formula and Compositions Data Set for Data-Driven Research

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.