Machine Learning Derived Blueprint for Rational Design of the Effective Single-Atom Cathode Catalyst of the Lithium-Sulfur Battery.
Zan LianMin YangFaheem JanBo LiPublished in: The journal of physical chemistry letters (2021)
The "shuttle effect" and sluggish kinetics at cathode significantly hinder the further improvements of the lithium-sulfur (Li-S) battery, a candidate of next generation energy storage technology. Herein, machine learning based on high-throughput density functional theory calculations is employed to establish the pattern of polysulfides adsorption and screen the supported single-atom catalyst (SAC). The adsorptions are classified as two categories which successfully distinguish S-S bond breaking from the others. Moreover, a general trend of polysulfides adsorption was established regarding of both kind of metal and the nitrogen configurations on support. The regression model has a mean absolute error of 0.14 eV which exhibited a faithful predictive ability. Based on adsorption energy of soluble polysulfides and overpotential, the most promising SAC was proposed, and a volcano curve was found. In the end, a reactivity map is supplied to guide SAC design of the Li-S battery.
Keyphrases
- density functional theory
- solid state
- molecular dynamics
- machine learning
- ion batteries
- aqueous solution
- high throughput
- reduced graphene oxide
- room temperature
- ionic liquid
- highly efficient
- gold nanoparticles
- artificial intelligence
- big data
- solar cells
- metal organic framework
- single cell
- electron transfer
- visible light
- deep learning