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

TL;DR

This paper introduces a machine learning protocol to rapidly predict Li-polysulphide adsorption on single-atom catalysts, aiding the design of more efficient and durable Li-S batteries by accelerating computational screening.

Contribution

The study develops and validates a machine learning approach to efficiently map potential energy surfaces for LiPS adsorption on SACs, significantly speeding up the screening process.

Findings

ML protocol accurately predicts adsorption energies.

Similar adsorption trends on Fe-N4-C and Zn-N4-C SACs.

Validated approach aligns with previous DFT results.

Abstract

Unlocking the design of Li-S batteries where no shuttle effects appears, and thus their energy storage capacity does not diminish over time, would enable the manufacturing of energy storage devices more performant than the current Li-ion commercial ones. Computational screening of Li-polysulphide (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 aid this process, we develop a machine learning protocol to accelerate the systematic mapping of dominant local minima found with DFT calculations, and, in turn, fast screen LiPS adsorption properties on SACs. We first validate the approach by probing the potential energy surface for Li-polysulphides adsorbed on graphene decorated with a Fe-N$_4$-C SAC bound to four nitrogen atoms. We identify minima whose binding energy is better or on par with the one previously reported in the literature. We then move to analyse the adsorption trends on Zn-N$_4$-C SAC and observe similar adsorption strength and behaviour with the Fe-N$_4$-C SAC, highlighting the good predictive power of our protocol.