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