Thermal conductivity of Li${}_3$PS${}_4$ solid electrolytes with ab initio accuracy

Abstract

The vast amount of computational studies on electrical conduction in solid-state electrolytes is not mirrored by comparable efforts addressing thermal conduction, which has been scarcely investigated despite its relevance to thermal management and (over)heating of batteries. The reason for this lies in the complexity of the calculations: on one hand, the diffusion of ionic charge carriers makes lattice methods formally unsuitable, due to the lack of equilibrium atomic positions needed for normal-mode expansion. On the other hand, the prohibitive cost of large-scale molecular dynamics (MD) simulations of heat transport in large systems at ab initio levels has hindered the use of MD-based methods. In this paper, we leverage recently developed machine-learning potentials targeting different ab initio functionals (PBEsol, r${}^2$SCAN, PBE0) and a state-of-the-art formulation of the Green-Kubo theory of heat transport in multicomponent systems to compute the thermal conductivity of a promising solid-state electrolyte, Li${}_3$PS${}_4$, in all its polymorphs ($\alpha$, $\beta$, and $\gamma$). By comparing MD estimates with lattice methods on the low-temperature, nondiffusive $\gamma$-Li${}_3$PS${}_4$, we highlight strong anharmonicities and negligible nuclear quantum effects, hence further justifying MD-based methods even for nondiffusive phases. Finally, for the ion-conducting $\alpha$ and $\beta$ phases, where the multicomponent Green-Kubo MD approach is mandatory, our simulations indicate a weak temperature dependence of the thermal conductivity, a glass-like behavior due to the effective local disorder characterizing these Li-diffusing phases.

11 pages, 5 figures

References in corpus (41)

• Canonical sampling through velocity-rescaling
• Restoring the density-gradient expansion for exchange in solids and surfaces
• Generalized gradient approximation for solids and their surfaces
• Phonons and related properties of extended systems from density-functional perturbation theory
• Gaussian Approximation Potentials: the accuracy of quantum mechanics, without the electrons
• On representing chemical environments
• Big Data meets Quantum Chemistry Approximations: The $\Delta$-Machine Learning Approach
• Unified theory of thermal transport in crystals and disordered solids
• Materials Cloud, a platform for open computational science
• i-PI 2.0: A Universal Force Engine for Advanced Molecular Simulations
• Force and heat current formulas for many-body potentials in molecular dynamics simulation with applications to thermal conductivity calculations
• Analysis of diffusion in solid state electrolytes through MD-simulations, improvement of the Li-ion conductivity in β-Li3PS4 as an example
• Modeling heat transport in crystals and glasses from a unified lattice-dynamical approach
• Langevin equation with colored noise for constant-temperature molecular dynamics simulations
• Fast and Accurate Uncertainty Estimation in Chemical Machine Learning
• Ab initio Green-Kubo Approach for the Thermal Conductivity of Solids
• High-throughput computational screening for solid-state Li-ion conductors
• Microscopic theory and quantum simulation of atomic heat transport
• Molecular dynamics simulations of heat transport using machine-learned potentials: A mini review and tutorial on GPUMD with neuroevolution potentials
• Anharmonicity Measure for Materials
• Heat transport in liquid water from first-principles and deep-neural-network simulations
• Efficient Anharmonic Lattice Dynamics Calculations of Thermal Transport in Crystalline and Disordered Solids
• Accurate thermal conductivities from optimally short molecular dynamics simulations
• Computational prediction of lattice thermal conductivity -- a comparison of molecular dynamics and Boltzmann transport approaches
• Accurate prediction of heat conductivity of water by a neuroevolution potential
• Nuclear quantum effects in ab initio dynamics: theory and experiments for lithium imide
• Efficient implementation of atom-density representations
• Anharmonicity in Thermal Insulators: An Analysis from First Principles
• Gauge invariance of thermal transport coefficients
• Theory and numerical simulation of heat transport in multi-component systems
• From Green-Kubo to the full Boltzmann kinetic approach to heat transport in crystals and glasses
• Transferability of neural network potentials for varying stoichiometry: phonons and thermal conductivity of Mn${}_x$Ge${}_y$ compounds
• Temperature- and vacancy-concentration-dependence of heat transport in Li${}_3$ClO from multi-method numerical simulations
• Heat and charge transport in H${}_2$O at ice-giant conditions from ab initio molecular dynamics simulations
• Robustness of Local Predictions in Atomistic Machine Learning Models
• Invariance principles in the theory and computation of transport coefficients
• Ab initio Green-Kubo simulations of heat transport in solids: Method and implementation
• Heat flux for semi-local machine-learning potentials
• Stress and heat flux via automatic differentiation
• QEHeat: An open-source energy flux calculator for the computation of heat-transport coefficients from first principles
• Self-interaction and transport of solvated electrons in molten salts

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