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

Published: 11 April 2023| Version 1 | DOI: 10.17632/9wx8v7djw3.1
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. The file named dataset.tar.gz provides all the data used for the ML model training and testing, while the file named ml_model.tar.gz provides the trained ML model using auto-sklearn. There are also a manual file named instructions_for_loading_data.txt and an example file named example.py that document and showcase how to load and use these data.

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Categories

Energy Materials, Machine Learning, Density Functional Theory

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