Recreation of the Periodic Table with an Unsupervised Machine Learning Algorithm

TL;DR

This paper introduces an unsupervised machine learning algorithm called PTG that can autonomously generate and organize periodic tables based on physicochemical properties of elements, replicating known periodic arrangements.

Contribution

The study presents a novel generative topographic mapping approach to recreate the periodic table from data, enabling flexible layouts and insights into element features.

Findings

Successfully generated various periodic table layouts

Revealed how element features are embedded in lower-dimensional space

Demonstrated the algorithm's ability to organize elements by periodicity

Abstract

In 1869, the first draft of the periodic table was published by Russian chemist Dmitri Mendeleev. In terms of data science, his achievement can be viewed as a successful example of feature embedding based on human cognition: chemical properties of all known elements at that time were compressed onto the two-dimensional grid system for tabular display. In this study, we seek to answer the question of whether machine learning can reproduce or recreate the periodic table by using observed physicochemical properties of the elements. To achieve this goal, we developed a periodic table generator (PTG). The PTG is an unsupervised machine learning algorithm based on the generative topographic mapping (GTM), which can automate the translation of high-dimensional data into a tabular form with varying layouts on-demand. The PTG autonomously produced various arrangements of chemical symbols, which organized a two-dimensional array such as Mendeleev's periodic table or three-dimensional spiral table according to the underlying periodicity in the given data. We further showed what the PTG learned from the element data and how the element features, such as melting point and electronegativity, are compressed to the lower-dimensional latent spaces.