---
title: Materials AI keeps failing the honest test. The fixes are in the data, not the model
description: Two preprints show general-purpose models flattered by their own evaluations, while small-data methods and better measurement tooling point to what actually works.
canonical: https://hangingcontext.com/stories/2026-11-11/materials-ml-honest-test/
last-updated: 2026-11-11
url: https://hangingcontext.com/stories/2026-11-11/materials-ml-honest-test/
published: 2026-11-11T12:37:50Z
edition: 2026-11-11T12
newsroom: research-science
beat: None
trust: confirmed
source_count: 4
cited_manifest_ids: ['1794398518831269926', '1794377214798156243', '1794390095025243376', '1794390095025243375']
---

# Materials AI keeps failing the honest test. The fixes are in the data, not the model

*Two preprints show general-purpose models flattered by their own evaluations, while small-data methods and better measurement tooling point to what actually works.*

Confirmed · 4 Manifests · Research & Science · Published 2026-11-11 12:37 UTC

## Key facts

- General-purpose machine-learned interatomic potentials failed to give quantitative carbon dioxide isotherm predictions for metal-organic frameworks in a preprint evaluating a set of models. (self-reported) [cites: 1794398518831269926]
- A random forest model scored R² 0.935 on random splits but −1.21 on own-paper holdouts for bismuth telluride Seebeck coefficient prediction. (self-reported) [cites: 1794377214798156243]
- TabPFN predicted electrolyte conductivity with 5.56% mean absolute percentage error on the AMPERE dataset using 40 labelled samples. (self-reported) [cites: 1794390095025243376]
- Bayesian optimisation guided by TabPFN cut experimental evaluations by 45.6% compared with random search. (self-reported) [cites: 1794390095025243376]
- GITTcalc automates lithium diffusion-coefficient extraction from GITT data and checks whether the method's underlying assumptions hold. (self-reported) [cites: 1794390095025243375]

## The honest-test problem

Two preprints reach the same uncomfortable conclusion about machine learning in materials science: the models look strong until the test is honest. In one, researchers evaluated a set of universal machine-learned interatomic potentials on carbon dioxide adsorption in metal-organic frameworks, the porous materials used in carbon capture, and found the general-purpose models could not produce quantitative isotherm predictions. In the other, a team building a leakage-safe dataset for thermoelectric materials found that a random forest model predicting the Seebeck coefficient of bismuth telluride scored an R² of 0.935 on random splits — and minus 1.21 when tested on papers it had never seen. [cites: 1794398518831269926, 1794377214798156243]

The pattern is the same in both cases: the evaluation flatters the model. The carbon-capture study traces the failure to errors in how the models describe the interaction between the framework and carbon dioxide, and between carbon dioxide molecules themselves, which sometimes cancel out by luck. The thermoelectric study shows that standard random splits inflate performance because near-duplicates leak across the train-test boundary. The lesson the two preprints share is that dataset composition matters more than model architecture — the carbon-capture authors say so directly — and that a benchmark that does not respect where data came from is not a benchmark. [cites: 1794398518831269926, 1794377214798156243]

## What survives an honest test

Not everything fails the honest test. A third preprint applies a tabular foundation model, TabPFN, to predicting electrolyte conductivity for batteries, working from as few as 40 labelled samples. On the AMPERE conductivity dataset the model reached a mean absolute percentage error of 5.56%, and when it guided Bayesian optimisation of electrolyte formulations it cut the number of experimental evaluations by 45.6% compared with random search — 38.2% fewer evaluations in the direct formulation search. The point is not the model. It is that small, well-chosen datasets can carry a real optimisation loop when the evaluation is set up to mean something. [cites: 1794390095025243376]

> **From Synorb** — Run the cited Manifests yourself. Every claim in this paper points to a Manifest id. Get a Synorb Key and read the Manifests behind it. The 72 Hour Agent Demo key needs no email. It lasts 72 hours and can pull up to 25 Manifests. It includes 5 Beacons; all of them are starter Beacons. [Get a key](https://synorb.com/connect/start)


The fourth piece of the picture is about the data going in. GITTcalc, a new Python tool described in a preprint, automates the extraction of lithium diffusion coefficients from galvanostatic intermittent titration technique experiments, the standard way of measuring how fast lithium moves through a battery electrode. Manual analysis of these experiments is slow and error-prone, and the tool adds built-in checks for whether the experiment's underlying assumptions hold, plus a graphical interface and compatibility with OriginLab, the analysis software many battery labs already use. Reproducible numbers in, honest benchmarks out — the tooling and the testing are two halves of the same fix. [cites: 1794390095025243375]

## Why this matters now

Materials informatics has spent several years importing methods from machine learning faster than it has imported machine learning's discipline about evaluation. That is starting to change, and the change is practical rather than philosophical: leakage-safe splits, source-labelled datasets, and small-data methods that say what they can do with 40 samples instead of implying they need far larger ones. For anyone funding or building in battery materials, carbon capture, or thermoelectrics, the question to ask of a model is no longer what it scores. It is what the test withheld. [cites: 1794377214798156243, 1794390095025243376]

There is also a narrower lesson for teams choosing where to spend. The carbon-capture preprint argues that training-data composition beats architecture — which suggests money spent curating the right adsorption data buys more than money spent on a bigger model. The electrolyte work suggests the same from the other direction: a foundation model used as a cheap stand-in for experiments only earns its keep if the search it guides is measured against random search, not against nothing. The field is converging on a simple standard: show the holdout, name the leak, and count the experiments saved. [cites: 1794398518831269926, 1794390095025243376]

## What is still unresolved

- Whether universal interatomic potentials can be fixed by better training data alone, or whether carbon capture needs specialised models
- Whether TabPFN's small-data electrolyte results transfer to other electrolyte chemistries beyond the datasets tested
- Whether leakage-safe benchmarks like TEBench get adopted beyond thermoelectrics

## Decision card

- **Useful for:** Battery materials teams choosing electrolyte screening methods; Carbon-capture researchers evaluating machine-learned potentials; Anyone benchmarking ML models on materials data
- **Not for:** Production deployment decisions — all four results are preprints, not peer-reviewed
- **Compare against:** Random-split benchmark scores; Random search baselines for formulation optimisation

## Provenance ledger

| Manifest id | Title | Source | Claims used | Published |
|---|---|---|---|---|
| `1794398518831269926` | How Ready Are Universal Machine-learned Interatomic Potentials for Carbon Capture Simulations? | [chemrxiv-carbon-capture-hydrogen-fuels](https://chemrxiv.org/doi/full/10.26434/chemrxiv.15006048/v2) | 1794398804062270596, 1794398804064270597, 1794398804066270598 | 11-11 00:00 |
| `1794377214798156243` | TEBench: a source-labelled thermoelectric data collection with leakage-safe splits and baselines for machine learning | [chemrxiv-nanomaterials-semiconductors-optoelectronics](https://chemrxiv.org/doi/full/10.26434/chemrxiv.15011944/v1) | 1794377448983156912, 1794377448980156911 | 11-11 00:00 |
| `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 |
| `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

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Cited Manifest ids:

- `1794398518831269926`
- `1794377214798156243`
- `1794390095025243376`
- `1794390095025243375`

```sh
for id in 1794398518831269926 1794377214798156243 1794390095025243376 1794390095025243375; do
  curl -s -H "Authorization: Bearer $SYNORB_KEY" "https://api.synorb.com/manifests/by-id/$id"
done
```

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