Classification and prediction of bulk densities of states and chemical attributes with machine learning techniques

作者：

Highlights：

• Classification and prediction of bulk densities of states and chemical attributes with machine learning techniques.

• Clusterization algorithm based on information obtained from the DOS.

• Four groups clearly differentiated, whose more representative elements are scandium, iron, gold and mercury.

• Possibility to univocally classify materials and predict in a fast and accurate manner different physical and chemical properties.

摘要

•Classification and prediction of bulk densities of states and chemical attributes with machine learning techniques.•Clusterization algorithm based on information obtained from the DOS.•Four groups clearly differentiated, whose more representative elements are scandium, iron, gold and mercury.•Possibility to univocally classify materials and predict in a fast and accurate manner different physical and chemical properties.

论文关键词：Density of states,Machine learning,Electronic structure,Chemical fingerprints

论文评审过程：Received 7 May 2021, Revised 27 July 2021, Accepted 9 August 2021, Available online 22 August 2021, Version of Record 22 August 2021.

论文官网地址：https://doi.org/10.1016/j.amc.2021.126587