Unsupervised meta-analysis on chemical elements and atomic energy prediction: A case study on the periodic table

Abstract

This paper presents an artificial classification and atomic energy correlation analysis of the chemical components. The choice of data mining is due to its robustness, which can explore intrinsic or hidden relationships between chemical components and their properties. The Mendeleev table is conceivably the earliest example of the data analysis technique in materials science. However, the classical periodic table represents the arrangement of chemical elements based on particular periodicities, which has the issue of property progression for a few chemical components. In this investigation, we utilized one of the unsupervised data mining methods (principal component analysis) to explore knowledge from the chemical components database based on all the prepared properties. The main objective is to make an artificial classification of chemical components depending on their accessible physical and energetic properties. The results revealed the effectiveness of the data mining method in appreciating the relationships between the variables and properties that offer a new approach to seeing a Mendeleev table. The final step of this work highlights the significance of predictive polynomials that permit the scientific community to make atomic total energy predictions for each chemical component, from helium to lawrencium.

Keywords

• Artificial classification
• Chemical elements
• Periodic table
• Atomic energy
• DFT
• LDA