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In 1869, the first draft of the periodic table was published by Russianchemist Dmitri Mendeleev. In terms of data science, his achievement can beviewed as a successful example of feature embedding based on human cognition:chemical properties of all known elements at that time were compressed onto thetwo-dimensional grid system for tabular display. In this study, we seek toanswer the question of whether machine learning can reproduce or recreate theperiodic table by using observed physicochemical properties of the elements. Toachieve this goal, we developed a periodic table generator (PTG). The PTG is anunsupervised machine learning algorithm based on the generative topographicmapping (GTM), which can automate the translation of high-dimensional data intoa tabular form with varying layouts on-demand. The PTG autonomously producedvarious arrangements of chemical symbols, which organized a two-dimensionalarray such as Mendeleev's periodic table or three-dimensional spiral tableaccording to the underlying periodicity in the given data. We further showedwhat the PTG learned from the element data and how the element features, suchas melting point and electronegativity, are compressed to the lower-dimensionallatent spaces.

Recreation of the Periodic Table with an Unsupervised Machine Learning Algorithm