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We applied the decision trees (random forest) machine-learning technique forthe large experimental materials dataset PAULING FILE, compiled from theworld's peer-reviewed literature. The training and validation data wereextracted from the hundreds of thousands of publications in materials science(1891-2017). Then, for the nearly 115'000 distinct inorganic compounds wepredicted 8 thermodynamic, mechanical, and electronic properties, using theonly crystalline structures as an input. For the predicted physical propertieswe observed certain periodical patterns in all unary, binary, ternary, andquaternary compounds. We also solved a reversed task of predicting the possiblecrystalline structure based on a given combination of values of the 8 mentionedproperties. Therefore our observations may play a role of the periodic table,formulated not for the chemical elements, but for the entire set of materials.

Quantitative trends in 8 physical properties of 115000 inorganic compounds gained by machine learning