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The reliability with Machine Learning (ML) techniques in novel materialsdiscovery often depend on the quality of the dataset, in addition to therelevant features used in describing the material. In this regard, the currentstudy presents and validates a newly processed materials dataset that can beutilized for benchmark ML analysis, as it relates to the prediction andclassification of deterministic target properties. Originally, the dataset wasextracted from the Open Quantum Materials Database (OQMD) and contains a robust16,323 samples of ABX3 inorganic perovskite structures. The dataset is tabularin form and is preprocessed to include sixty-one generalized input featuresthat broadly describes the physicochemical, stability/geometrical, and DensityFunctional Theory (DFT) target properties associated with the elemental ionicsites in a three-dimensional ABX3 polyhedral. For validation, four different MLmodels are employed to predict three distinctive target properties, namely:formation energy, energy band gap, and crystal system. On experimentation, thebest accuracy measurements are reported at 0.013 eV/atom MAE, 0.216 eV MAE, and85% F1, corresponding to the formation energy prediction, band gap predictionand crystal system multi-classification, respectively. Moreover, the realizedresults are compared with previous literature and as such, affirms theresourcefulness of the current dataset for future benchmark materials analysisvia ML techniques. The preprocessed dataset and source codes are openlyavailable to download from github.com/chenebuah/ML_abx3_dataset.

An inorganic ABX3 perovskite materials dataset for target property prediction and classification using machine learning