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Issue 44, 2020
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Lithium adsorption on 2D transition metal dichalcogenides: towards a descriptor for machine learned materials design

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Abstract

The adsorption properties of alkali metal atoms on two dimensional (2D) materials play an important role in the performance of batteries, catalysts, and sensors. In view of designing materials for such applications, we provide a comparative study of the adsorption of alkali metal atoms on different single-layer 2D transition metal dichalcogenides (TMDCs). Based on quantum mechanical calculations, we find that the adsorption energies of alkali metal atoms on 2D TMDCs are strongly and linearly correlated with the energy of the lowest unoccupied states Image ID:d0ta04834h-t1.gif of the materials. This relation is associated with the strong ionization of alkali metal atoms. We propose and demonstrate the ELUS of TMDCs as a descriptor for predicting adsorption energies. As long as the adsorption is controlled by charge transfer providing a phenomenological model for connecting the electronic details of a material to its interaction with other atomic/molecular species this predictor is expected to be transferable. Using a simple regression model, we could predict the adsorption properties of other alkali atoms on a variety of 2D TMDCs from three groups of the periodic table. Our results strongly support the use of the energy of the lowest unoccupied states as a novel efficient descriptor for data-driven materials design.

Graphical abstract: Lithium adsorption on 2D transition metal dichalcogenides: towards a descriptor for machine learned materials design

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Article information


Submitted
10 May 2020
Accepted
07 Oct 2020
First published
02 Nov 2020

J. Mater. Chem. A, 2020,8, 23511-23518
Article type
Paper

Lithium adsorption on 2D transition metal dichalcogenides: towards a descriptor for machine learned materials design

M. Dou and M. Fyta, J. Mater. Chem. A, 2020, 8, 23511
DOI: 10.1039/D0TA04834H

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