Eleftherios Andritsos^1*, Kevin Rossi^2*

1 Department of Physics, King’s College London, Strand, London, WC2R 2LS, UK

2 Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne (EPFL), CH-1951 Sion, Valais, Switzerland

* Corresponding authors emails: elefandas@gmail.com, kevin.rossi@epfl.ch

DOI10.24435/materialscloud:zz-w3 [version v2]

Publication date: Apr 05, 2022

How to cite this record

Eleftherios Andritsos, Kevin Rossi, Accelerating the theoretical study of Li-polysulphide adsorption on single-atom catalysts via machine learning approaches, Materials Cloud Archive 2022.48 (2022), https://doi.org/10.24435/materialscloud:zz-w3

Description

Li–S batteries are a promising alternative to Li-ion batteries, offering large energy storage capacity and wide operating temperature range. However, their performance is heavily affected by the Li-polysulphide (LiPS) shuttling. Computational screening of LiPS adsorption on single-atom catalyst (SAC) substrates is of great aid to the design of Li–S batteries which are robust against the LiPS shuttling from the cathode to the anode and the electrolyte. To facilitate this process, we develop a machine learning (ML) protocol to accelerate the systematic mapping of dominant local energy minima found with calculations based on the density functional theory (DFT), and, in turn, fast screening of LiPS adsorption properties on SACs. We first validate the approach by probing the potential energy surface for LiPS adsorbed on graphene decorated with a Fe–N4–C SAC. We identify minima whose binding energies are better or on par with the one previously reported in the literature. We then move to analyse the adsorption trends on Zn–N4–C SAC and observe similar adsorption strength and behaviour with the Fe–N4–C SAC, highlighting the good predictive power of our protocol. Our approach offers a comprehensive and computationally efficient alternative to conventional approaches studying LiPS adsorption

Materials Cloud sections using this data

Files

File name	Size	Description
LiPS-GCH_22_03_1.ipynb MD5md5:607168f031dec8327115a05cfe6c6daf	35.3 KiB	GCH Li2S notebook example
LiPS-notebooks_04_03_clean.ipynb MD5md5:acea76542027822b94f375640deac0bc	88.2 KiB	Li2S notebook model train test and validation
FeLIPS_data.tar.gz MD5md5:e02c63af54a4478722cc82e5754d569f	1.5 MiB	training structure with energy labels
README.txt MD5md5:495f3a8f3673b9025df6a3377442273d	203 Bytes	readme file
fe1n4c66-li2s.cell MD5md5:1911f4716dab03abf4664145f6221821	3.8 KiB	input file 1 CASTEP example
fe1n4c66-li2s.param MD5md5:81f00f321c14eef15bf8847d8a4c7e5c	586 Bytes	input file 2 CASTEP example

License

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External references

Keywords

batteries minima search generalised convex hull