Abstract

Understanding and optimizing polysulfide adsorption and conversion processes are critical to mitigating shuttle effects and sluggish redox kinetics in lithium-sulfur batteries (LSBs). Here, we introduce a machine-learning-accelerated framework, PACE, that integrates Machine Learning Interatomic Potentials (MLIPs) with Density Functional Theory (DFT) to systematically explore adsorption configurations and energetics of a series of N₆-coordinated dual-atom catalysts (DACs). Our results demonstrate that, compared with single-atom catalysts, DACs exhibit improved LiPS adsorption and redox conversion through cooperative metal-sulfur interactions and electronic coupling between adjacent metal centers. Among all DACs, Fe-Ni and Fe-Pt show optimal catalytic performance, due to their optimal adsorption energies (-1.0 to -2.3 eV), low free-energy barriers (≤0.4 eV) for the Li₂S₂ to Li₂S conversion, and facile Li₂S decomposition barriers (≤1.0 eV). To accelerate catalyst screening, we further developed a machine learning (ML) regression model trained on DFT-calculated data to predict the Gibbs free energy (ΔG) of Li₂Sn adsorption using physically interpretable descriptors. The Gradient Boosting Regression (GBR) model yields an R² of 0.85 and an MAE of 0.26 eV, enabling the rapid prediction of ΔG for unexplored DACs. Electronic-structure analyses reveal that the superior performance originates from the optimal d-band alignment and S-S bond polarization induced by the cooperative effect of dual metal centres. This dual ML-DFT framework demonstrates a generalizable, data-driven design strategy for the rational discovery of efficient catalysts for next-generation LSBs.

Abstract

Understanding and optimizing polysulfide adsorption and conversion processes are critical to mitigating shuttle effects and sluggish redox kinetics in lithium-sulfur batteries (LSBs). Here, we introduce a machine-learning-accelerated framework, PACE, that integrates Machine Learning Interatomic Potentials (MLIPs) with Density Functional Theory (DFT) to systematically explore adsorption configurations and energetics of a series of N₆-coordinated dual-atom catalysts (DACs). Our results demonstrate that, compared with single-atom catalysts, DACs exhibit improved LiPS adsorption and redox conversion through cooperative metal-sulfur interactions and electronic coupling between adjacent metal centers. Among all DACs, Fe-Ni and Fe-Pt show optimal catalytic performance, due to their optimal adsorption energies (-1.0 to -2.3 eV), low free-energy barriers (≤0.4 eV) for the Li₂S₂ to Li₂S conversion, and facile Li₂S decomposition barriers (≤1.0 eV). To accelerate catalyst screening, we further developed a machine learning (ML) regression model trained on DFT-calculated data to predict the Gibbs free energy (ΔG) of Li₂Sn adsorption using physically interpretable descriptors. The Gradient Boosting Regression (GBR) model yields an R² of 0.85 and an MAE of 0.26 eV, enabling the rapid prediction of ΔG for unexplored DACs. Electronic-structure analyses reveal that the superior performance originates from the optimal d-band alignment and S-S bond polarization induced by the cooperative effect of dual metal centres. This dual ML-DFT framework demonstrates a generalizable, data-driven design strategy for the rational discovery of efficient catalysts for next-generation LSBs.

Abstract

Understanding and optimizing polysulfide adsorption and conversion processes are critical to mitigating shuttle effects and sluggish redox kinetics in lithium-sulfur batteries (LSBs). Here, we introduce a machine-learning-accelerated framework, PACE, that integrates Machine Learning Interatomic Potentials (MLIPs) with Density Functional Theory (DFT) to systematically explore adsorption configurations and energetics of a series of N₆-coordinated dual-atom catalysts (DACs). Our results demonstrate that, compared with single-atom catalysts, DACs exhibit improved LiPS adsorption and redox conversion through cooperative metal-sulfur interactions and electronic coupling between adjacent metal centers. Among all DACs, Fe-Ni and Fe-Pt show optimal catalytic performance, due to their optimal adsorption energies (-1.0 to -2.3 eV), low free-energy barriers (≤0.4 eV) for the Li₂S₂ to Li₂S conversion, and facile Li₂S decomposition barriers (≤1.0 eV). To accelerate catalyst screening, we further developed a machine learning (ML) regression model trained on DFT-calculated data to predict the Gibbs free energy (ΔG) of Li₂Sn adsorption using physically interpretable descriptors. The Gradient Boosting Regression (GBR) model yields an R² of 0.85 and an MAE of 0.26 eV, enabling the rapid prediction of ΔG for unexplored DACs. Electronic-structure analyses reveal that the superior performance originates from the optimal d-band alignment and S-S bond polarization induced by the cooperative effect of dual metal centres. This dual ML-DFT framework demonstrates a generalizable, data-driven design strategy for the rational discovery of efficient catalysts for next-generation LSBs.

Abstract

Understanding and optimizing polysulfide adsorption and conversion processes are critical to mitigating shuttle effects and sluggish redox kinetics in lithium-sulfur batteries (LSBs). Here, we introduce a machine-learning-accelerated framework, PACE, that integrates Machine Learning Interatomic Potentials (MLIPs) with Density Functional Theory (DFT) to systematically explore adsorption configurations and energetics of a series of N₆-coordinated dual-atom catalysts (DACs). Our results demonstrate that, compared with single-atom catalysts, DACs exhibit improved LiPS adsorption and redox conversion through cooperative metal-sulfur interactions and electronic coupling between adjacent metal centers. Among all DACs, Fe-Ni and Fe-Pt show optimal catalytic performance, due to their optimal adsorption energies (-1.0 to -2.3 eV), low free-energy barriers (≤0.4 eV) for the Li₂S₂ to Li₂S conversion, and facile Li₂S decomposition barriers (≤1.0 eV). To accelerate catalyst screening, we further developed a machine learning (ML) regression model trained on DFT-calculated data to predict the Gibbs free energy (ΔG) of Li₂Sn adsorption using physically interpretable descriptors. The Gradient Boosting Regression (GBR) model yields an R² of 0.85 and an MAE of 0.26 eV, enabling the rapid prediction of ΔG for unexplored DACs. Electronic-structure analyses reveal that the superior performance originates from the optimal d-band alignment and S-S bond polarization induced by the cooperative effect of dual metal centres. This dual ML-DFT framework demonstrates a generalizable, data-driven design strategy for the rational discovery of efficient catalysts for next-generation LSBs.