Data for 'A systematic study on the metallophilicity of ordered five-atomic-layer MXenes using high-throughput automated workflow and machine learning'

Published: 22 August 2023| Version 2 | DOI: 10.17632/9wx8v7djw3.2
Contributor:
Xiang Feng

Description

These files accompany the manuscript 'A systematic study on the metallophilicity of ordered five-atomic-layer MXenes using high-throughput automated workflow and machine learning'. In this manuscript, the metallophilicity of ordered five-atomic-layer MXenes to a total of eight kinds of metal (Li, Na, K, Mg, Ca, Fe, Zn, and Al) anodes is investigated using density functional theory (DFT) and machine learning (ML) schemes.

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Energy Materials, Machine Learning, Density Functional Theory

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