Predicting and discovering fast lithium solid electrolytes
Predict room-temperature ionic conductivity of solid lithium-ion conductors from structure and composition, and propose new candidates that beat known sulfides on conductivity plus stability.
Cite
@misc{cairn-solid-state-electrolytes,
title = {Predicting and discovering fast lithium solid electrolytes},
author = {{Cairn Commons contributors}},
howpublished = {\url{https://cairn-commons.com/problems/solid-state-electrolytes}},
year = {2026},
note = {Open problem on Cairn Commons, CC BY 4.0. Accessed 2026-11-12}
} Also: CITATION.cff · Atom feed of results
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What a result looks like: C-16, a newcomer working with an AI model found C(85,24,3) ≤ 76 (previous best 77), and a computer verified it within minutes. A documented dead end counts too.
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Current state
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The problem
All-solid-state batteries need an electrolyte that conducts lithium quickly at room temperature and is stable against the electrodes. The open computational question: can room-temperature ionic conductivity (and the electrochemical stability window) be predicted reliably enough from a crystal structure to prioritise candidates, given that measured conductivities span many orders of magnitude and depend on processing?
Known status. Li10GeP2S12 reached 12 mS/cm at room temperature (Kamaya, Kanno et al., Nature Materials 2011), and the LGPS-family composition Li9.54Si1.74P1.44S11.7Cl0.3 reached 25 mS/cm (Kato, Hori, Kanno et al., Nature Energy 2016), which also demonstrated cells cycling at 18 C. For model development, OBELiX (arXiv:2502.14234, 2025) provides about 600 synthesised solid electrolytes with expert-curated room-temperature conductivities, roughly 320 of them with full crystallographic information files — a small, noisy, highly imbalanced target that exposes how weakly current models extrapolate.
What counts as progress
- Reproducible predictive models trained and evaluated on public data (e.g. OBELiX) with code and splits released, including composition- or structure-family-held-out splits rather than random ones.
- Molecular-dynamics studies (ab initio or with machine-learned potentials) that compute diffusivity and activation energy for named compounds, with trajectories long enough to report error bars, and inputs published.
- Candidate lists from screens over public structure databases, with the criteria (conductivity proxy, stability window, phase stability, cost, earth-abundance) and full ranked output released.
- Documented negative results: a descriptor or potential that fails for a structural family, shown quantitatively.
How it is checked. A reviewer re-runs the training or MD workflow, checks that reported conductivities come with statistical uncertainty and stated temperature extrapolation, and confirms that held-out materials were genuinely unseen.