Progress, challenges and perspectives of computational studies on glassy superionic conductors for solid-state batteries

Abstract

Sulfide-based glasses and glass-ceramics showing high ionic conductivities and excellent mechanical properties are considered as promising solid-state electrolytes. Nowadays, the computational material techniques with the advantage of low research cost are being widely utilized for understanding, effectively screening and discovering of battery materials. In consideration of the rising importance and contributions of computational studying on the glassy SSE materials, here, this work summarizes the common computational methods utilized for studying the amorphous inorganic materials, review the recent progress in computational investigations of the lithium and sodium sulfide-type glasses for solid-state batteries, and outlines our understandings of the challenges and future perspective on them. This review would facilitate and accelerate the future computational screening and discovering more glassy-state SSE materials for the solid-state batteries.

References in corpus (17)

• CP2K: An Electronic Structure and Molecular Dynamics Software Package -- Quickstep: Efficient and Accurate Electronic Structure Calculations
• ANI-1: An extensible neural network potential with DFT accuracy at force field computational cost
• Machine Learning of Accurate Energy-Conserving Molecular Force Fields
• A Performance and Cost Assessment of Machine Learning Interatomic Potentials
• On-the-fly machine learning force field generation: Application to melting points
• Machine-learning based interatomic potential for amorphous carbon
• Accelerating crystal structure prediction by machine-learning interatomic potentials with active learning
• Analysis of diffusion in solid state electrolytes through MD-simulations, improvement of the Li-ion conductivity in β-Li3PS4 as an example
• Constructing first-principles phase diagrams of amorphous LixSi using machine-learning-assisted sampling with an evolutionary algorithm
• Accelerated Discovery of Efficient Solar-cell Materials using Quantum and Machine-learning Methods
• Quantum-Accurate Spectral Neighbor Analysis Potential Models for Ni-Mo Binary Alloys and FCC Metals
• Deep Potential generation scheme and simulation protocol for the Li10GeP2S12-type superionic conductors
• Influence of lattice dynamics on lithium-ion conductivity: A first-principles study
• Anion charge-lattice volume dependent Li ion migration in compounds with the face-centered cubic anion frameworks
• Revealing the Atomic Structure of Silicate Glasses by Force-Enhanced Atomic Refinement
• Dynamics of Na Ion in the Amorphous Na2Si2O5 Using Quasielastic Neutron Scattering and Molecular Dynamics Simulations
• AI-Aided Mapping of the Structure-Composition-Conductivity Relationships of Glass-Ceramic Lithium Thiophosphate Electrolytes