A Critical Review of Theoretical Approaches to Compute Ionic Conductivity in Solid-State Electrolytes: The Case Study of Li3YCl6 and Li3YBr6
- 1IFP Energies nouvelles, 1-4 avenue du Bois Préau, Rueil-Malmaison, France, 92852
- 2Inorganic Material synthesis, IFP Energies nouvelles, Rond Point de l’échangeur de Solaize, BP 3, Solaize, France, 69360
- 3Center for Molecular Modeling, Ghent University, Technologiepark-Zwijnaarde 46, Zwijnaarde, Belgium, 9052
Abstract
The development of next-generation batteries strongly depends on the design of electrolytes that ensure efficient ionic transport. In this context, computational in silico approaches have become increasingly important. Among them, molecular dynamics simulations are particularly powerful for investigating constitutive elements of batteries and providing a deeper understanding of structure-property relationships in solid-state electrolytes. Yet, for these systems, obtaining accurate ionic conductivity and activation energy is not straightforward as finding a force field to accurately describe the system and a robust workflow is challenging. In this work, a classical force field (CFF), based on the SoftBV bond-valence approach, and a machine learning potential (MLP) trained on density functional theory (DFT) energies and forces using the MACE neural network architecture, were optimized and compared to investigate the two solid electrolytes, Li3YCl6 (LYC) and Li3YBr6 (LYB). Our study proposes an integrated workflow (structure generation, property analysis with relevant statistical sampling) using standard methodologies based on the Einstein equations and a new method called STACIE[Toraman et al., J. Chem. Inf. Model., 65, 19, 2025, DOI:10.1021/acs.jcim.5c01475] (STable AutoCorrelation Integral Estimator) deriving these properties through the analysis of power spectral densities for transport property evaluation. Based on the evaluation and analysis of the contribution of the crosscorrelations between mobile ions, we provide clear rules to validate the use of Nernst-Einstein’s approximation to estimate the ionic conductivity. Results show trends in agreement with the literature. Notably the correct order of the conductivity activation energies Ea(LYC) > Ea(LYB), observed experimentally, is maintained. Overall, while CFF can provide an adequate representation when guided by experimental data, the strength of the MACE potential lies in its predictive capability. Using configurations generated by DFT calculations, the MLP generates trajectories with accurate crystallographic features from which realistic transport properties are extracted. Compared to their CFF counterparts, MLP results also show better mechanical stability at higher temperature for LYB, a softer system based on the analysis of solid-phase mechanical properties.
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BibTeX
@misc{rondeau2026critical,
title = {{A Critical Review of Theoretical Approaches to Compute Ionic Conductivity in Solid-State Electrolytes: The Case Study of Li3YCl6 and Li3YBr6}},
author = {Amély Rondeau and Namrata Jaykhedkar and Theodorus De Bruin and Manuel Corral Valero and Toon Verstraelen and Carlos Nieto-Draghi},
howpublished = {ChemRxiv},
year = {2026},
month = sep,
doi = {10.26434/chemrxiv.15009505/v1},
url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009505/v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - A Critical Review of Theoretical Approaches to Compute Ionic Conductivity in Solid-State Electrolytes: The Case Study of Li3YCl6 and Li3YBr6
AU - Amély Rondeau
AU - Namrata Jaykhedkar
AU - Theodorus De Bruin
AU - Manuel Corral Valero
AU - Toon Verstraelen
AU - Carlos Nieto-Draghi
PY - 2026
DA - 2026/09/27
PB - ChemRxiv
DO - 10.26434/chemrxiv.15009505/v1
UR - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009505/v1
AB - The development of next-generation batteries strongly depends on the design of electrolytes that ensure efficient ionic transport. In this context, computational in silico approaches have become increasingly important. Among them, molecular dynamics simulations are particularly powerful for investigating constitutive elements of batteries and providing a deeper understanding of structure-property relationships in solid-state electrolytes. Yet, for these systems, obtaining accurate ionic conductivity and activation energy is not straightforward as finding a force field to accurately describe the system and a robust workflow is challenging. In this work, a classical force field (CFF), based on the SoftBV bond-valence approach, and a machine learning potential (MLP) trained on density functional theory (DFT) energies and forces using the MACE neural network architecture, were optimized and compared to investigate the two solid electrolytes, Li3YCl6 (LYC) and Li3YBr6 (LYB). Our study proposes an integrated workflow (structure generation, property analysis with relevant statistical sampling) using standard methodologies based on the Einstein equations and a new method called STACIE[Toraman et al., J. Chem. Inf. Model., 65, 19, 2025, DOI:10.1021/acs.jcim.5c01475] (STable AutoCorrelation Integral Estimator) deriving these properties through the analysis of power spectral densities for transport property evaluation. Based on the evaluation and analysis of the contribution of the crosscorrelations between mobile ions, we provide clear rules to validate the use of Nernst-Einstein’s approximation to estimate the ionic conductivity. Results show trends in agreement with the literature. Notably the correct order of the conductivity activation energies Ea(LYC) > Ea(LYB), observed experimentally, is maintained. Overall, while CFF can provide an adequate representation when guided by experimental data, the strength of the MACE potential lies in its predictive capability. Using configurations generated by DFT calculations, the MLP generates trajectories with accurate crystallographic features from which realistic transport properties are extracted. Compared to their CFF counterparts, MLP results also show better mechanical stability at higher temperature for LYB, a softer system based on the analysis of solid-phase mechanical properties.
ER -