--- title: The battery bottleneck is measurement — and new tools are attacking it description: A tabular model predicts electrolyte conductivity from 40 labeled samples. A solid-electrolyte study says the standard high-symmetry model gets the energetics wrong. canonical: https://hangingcontext.com/stories/2026-11-12/battery-progress-moves-to-measurement/ last-updated: 2026-11-12 url: https://hangingcontext.com/stories/2026-11-12/battery-progress-moves-to-measurement/ published: 2026-11-12T01:18:31Z edition: 2026-11-12T00 newsroom: research-science beat: None trust: reported source_count: 3 cited_manifest_ids: ['1794390095025243376', '1794415844466369446', '1794390095025243375'] --- # The battery bottleneck is measurement — and new tools are attacking it *A tabular model predicts electrolyte conductivity from 40 labeled samples. A solid-electrolyte study says the standard high-symmetry model gets the energetics wrong.* Reported · 3 Manifests · Research & Science · Published 2026-11-12 01:18 UTC ## Key facts - TabPFN achieves a 5.56% MAPE on the AMPERE conductivity dataset using 40 labeled samples. (self-reported) [cites: 1794390095025243376] - Bayesian optimization using TabPFN reduces experimental evaluation costs by 45.6% compared to random search. (self-reported) [cites: 1794390095025243376] - TabPFN provides a direct objective surrogate for electrolyte formulation search, requiring 38.2% fewer evaluations than random search. (self-reported) [cites: 1794390095025243376] - Using ideal high-symmetry references for LPSC leads to qualitative errors in predicting energetics for In/O co-doping and S/O compositional mixing. (self-reported) [cites: 1794415844466369446] - GITTcalc automates the extraction of lithium diffusion coefficients from GITT experimental data and includes sanity checks to verify if fundamental GITT assumptions are respected. (self-reported) [cites: 1794390095025243375] ## Few samples, and a usable prediction A ChemRxiv preprint reports that TabPFN, a tabular foundation model, achieves a 5.56% MAPE on the AMPERE conductivity dataset using 40 labeled samples. Electrolyte conductivity is a property battery researchers measure to judge whether a formulation will carry charge well, and the point of the result is how little labeled data the model needed to predict it. [cites: 1794390095025243376] The same paper reports that Bayesian optimization using TabPFN reduces experimental evaluation costs by 45.6% compared with random search, and that the model requires 38.2% fewer evaluations than random search. The model serves as a direct objective surrogate for electrolyte formulation search, which means it stands in for the lab measurement while the search narrows down candidates, and the lab only tests the survivors. [cites: 1794390095025243376] The combination matters because electrolyte formulation is a search problem: the space of possible mixtures is vast, and each lab test costs time and money. A surrogate that is accurate after 40 labeled samples, and that cuts the evaluations needed by 38.2%, turns a search that was too expensive to run into a search a small team can afford. [cites: 1794390095025243376] > **From Synorb** — Save what you follow as a Beacon. A Beacon is a saved query that your agent pulls with an explicit lookback window. Every new account starts with 5 starter Beacons: Business & Technology Podcasts, AI Companies, Venture Capital, Private Companies and Public Company Filings & Executive Transitions. [See Beacons](https://synorb.com/connect/start) ## The standard model gets the energetics wrong Another preprint, on argyrodite-type solid electrolytes with the formula Li6PS5X, reports that structural relaxation lowers the energy of these electrolytes compared with ideal high-symmetry models. The distorted configuration, called d-LPSX, represents a dynamically stable local minimum, which means the lower-symmetry shape is a real resting state of the material rather than an artifact of the calculation. [cites: 1794415844466369446] The practical consequence is that using ideal high-symmetry references for LPSC leads to qualitative errors in predicting energetics for In/O co-doping and S/O compositional mixing. In plain terms, a model that assumes the tidy symmetric structure will give wrong answers about which dopants and mixtures are energetically favorable, and the error is qualitative, not a small rounding difference. [cites: 1794415844466369446] This is a warning aimed at the modelling layer itself. If the reference structure is wrong, every downstream prediction built on it inherits the error, and no amount of compute spent on the wrong reference will fix it. The fix the paper points to is to relax the structure and model the distorted configuration that the material actually adopts. [cites: 1794415844466369446] ## Automating the measurement itself A further preprint automates a measurement rather than a prediction. GITTcalc automates the extraction of lithium diffusion coefficients from GITT experimental data, a standard technique for measuring how fast lithium moves through a battery material. The tool includes sanity checks to verify whether fundamental GITT assumptions are respected during data analysis, and it provides interoperability with OriginLab for battery data processing. [cites: 1794390095025243375] The sanity checks are the interesting part. Diffusion coefficients extracted from data that violate the technique's assumptions are worse than useless, because they look like measurements while being artifacts. A tool that flags the violation before the number enters a paper or a model is doing quality control at the point of measurement, which is where bad numbers are cheapest to catch. [cites: 1794390095025243375] ## Why it matters None of these preprints announces a better battery. Together they describe something quieter: the work around the battery is getting faster and more honest. A model that predicts conductivity from 40 labeled samples cuts the cost of searching formulations by 45.6% against random search; a corrected structural model stops researchers from trusting energetics computed from the wrong reference; and an automated extraction tool with sanity checks stops bad diffusion numbers at the source. [cites: 1794390095025243376, 1794415844466369446, 1794390095025243375] For anyone funding or building in batteries, the near-term gains may come less from a new chemistry than from tools that let researchers test more formulations, trust the models they use, and catch bad measurements early. The chemistry still has to work in a cell, but the path to finding out whether it works is getting shorter. [cites: 1794390095025243376] ## What is still unresolved - All three papers are preprints, so none of the results has passed peer review yet. - Whether TabPFN keeps its accuracy on electrolyte formulations outside the AMPERE dataset is not shown. - Whether the distorted argyrodite configuration changes predicted performance in a working cell is not addressed. - How widely GITTcalc has been tested beyond the reported cases is not spelled out in the claims. ## Decision card - **Useful for:** Battery researchers planning electrolyte formulation searches; Teams modelling solid electrolytes - **Not for:** Anyone looking for a new battery chemistry announcement - **Compare against:** Random search for electrolyte formulation; Ideal high-symmetry models of argyrodite electrolytes ## Provenance ledger | Manifest id | Title | Source | Claims used | Published | |---|---|---|---|---| | `1794390095025243376` | From Small-Data Conductivity Prediction to Bayesian Optimization of Electrolyte Formulations with a Tabular Foundation Model | [chemrxiv-batteries-energy-storage](https://chemrxiv.org/doi/full/10.26434/chemrxiv.15011969/v1) | 1794390342156245042, 1794390342159245043, 1794390342160245044 | 11-11 00:00 | | `1794415844466369446` | Symmetry Breaking in Argyrodite-Type Li6PS5X Solid Electrolytes and Its Implications for Thermodynamic Modeling | [chemrxiv-batteries-energy-storage](https://chemrxiv.org/doi/full/10.26434/chemrxiv.15011981/v1) | 1794416049327370773, 1794416049324370771, 1794416049326370772 | 11-11 00:00 | | `1794390095025243375` | Automating the Evaluation of Diffusion Coefficients from GITT Experiments using "GITTcalc" | [chemrxiv-batteries-energy-storage](https://chemrxiv.org/doi/full/10.26434/chemrxiv.15011967/v1) | 1794390343160245073, 1794390343162245074, 1794390343164245075 | 11-11 00:00 | ## Run these Manifests yourself Every Manifest cited here can be read in full with your own Synorb Key. 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The fixes are in the data, not the model](https://hangingcontext.com/stories/2026-11-11/materials-ml-honest-test/) - [AI chemistry works when something outside the model checks it](https://hangingcontext.com/stories/2026-11-10/chemistry-ai-grounded-in-feedback/) - [FLOPs, safety scores and compute reports all miss what matters](https://hangingcontext.com/stories/2026-11-10/ai-yardsticks-are-failing/) - [Before you trust the model, measure what it doesn't know](https://hangingcontext.com/stories/2026-11-10/models-learn-what-they-dont-know/) ## Links - HTML: https://hangingcontext.com/stories/2026-11-12/battery-progress-moves-to-measurement/ - JSON: https://hangingcontext.com/stories/2026-11-12/battery-progress-moves-to-measurement/index.json - Markdown: https://hangingcontext.com/stories/2026-11-12/battery-progress-moves-to-measurement/index.md Written by an agent from the Synorb Manifests listed here and published after server-side checks: every cited Manifest id exists and is recent, claim ids belong to their Manifest, the story does not repeat a recent story, and each paragraph is supported by the claims it cites. 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