There is a newer version of the record available.

Published April 1, 2022 | Version v1
Dataset Open

Accelerating the theoretical study of Li-polysulphide adsorption on single-atom catalysts via machine learning approaches

  • 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

* Contact person

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

Files

File preview

files_description.md

All files

Files (1.6 MiB)

Name Size
md5:bfdfbc4380e6fc27209e8903c0ccdd69
354 Bytes Preview Download
md5:e02c63af54a4478722cc82e5754d569f
1.5 MiB Download
md5:607168f031dec8327115a05cfe6c6daf
35.3 KiB Preview Download
md5:acea76542027822b94f375640deac0bc
88.2 KiB Preview Download
md5:495f3a8f3673b9025df6a3377442273d
203 Bytes Preview Download

References

Preprint (Preprint where the data is discussed)
E. Lefterios, K. Rossi, ArXiV, 2112.11537 (2021), doi: 10.48550/arXiv.2112.11537

Preprint (Preprint where the data is discussed)
E. Lefterios, K. Rossi, ArXiV, 2112.11537 (2021)