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Recreation of the periodic table with an unsupervised machine learning algorithm
Scientific Reports ( IF 4.6 ) Pub Date : 2021-02-26 , DOI: 10.1038/s41598-021-81850-z
Minoru Kusaba , Chang Liu , Yukinori Koyama , Kiyoyuki Terakura , Ryo Yoshida

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 a 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, 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.



中文翻译:

使用无监督机器学习算法来创建元素周期表

1869年,元素周期表的初稿由俄罗斯化学家德米特里·门捷列夫(Dmitri Mendeleev)出版。在数据科学方面,他的成就可以看作是基于人类认知的特征嵌入的成功范例:当时所有已知元素的化学性质都被压缩到二维网格系统中以表格形式显示。在这项研究中,我们试图回答使用机器学习的元素的理化特性,机器学习是否可以复制或重新创建元素周期表的问题。为了实现此目标,我们开发了周期表生成器(PTG)。PTG是一种基于生成式地形图的无监督机器学习算法,该算法可以将高维数据自动转换为具有按需变化布局的表格形式。PTG自动产生各种化学符号排列,根据给定数据中的基本周期性,它们组织了二维数组,例如Mendeleev的周期表或三维螺旋表。我们进一步展示了PTG从元素数据中学到的知识以及元素特征(例如熔点和电负性)如何压缩到低维潜在空间。

更新日期:2021-02-26
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