Computational references are not experiments: pre-registered validation of machine-learned sodium-cathode voltages
Mirrored from arXiv — Machine Learning for archival readability. Support the source by reading on the original site.
Condensed Matter > Materials Science
arXiv:2606.23725 (cond-mat)
[Submitted on 19 Jun 2026]
Title:Computational references are not experiments: pre-registered validation of machine-learned sodium-cathode voltages
Authors:Krishna Teja Vepa
View a PDF of the paper titled Computational references are not experiments: pre-registered validation of machine-learned sodium-cathode voltages, by Krishna Teja Vepa
View PDF
Abstract:Machine-learning screens for battery materials are trained and judged almost entirely against computed reference voltages, and those references carry their own systematic errors. We report a case in which this matters quantitatively: our own screening stack (a graph-network voltage screen, a prior-art triage layer, and a local PBE+U bench) fails pre-registered validation against experiment-anchored literature values. Verdict thresholds, failure modes, and the primary metric were committed before analysis. On an operator-audited set of known Na-ion cathodes (n = 6 after one documented exclusion; verdict unchanged at n = 7), the raw held-out mean absolute error was 0.67 V, the pre-registered conservative metric, the upper 95% confidence bound of the cross-validated bias-corrected error, was 1.09 V, and the residual was strongly voltage-dependent (r = -0.94), so no additive calibration is valid. On the two compounds where prediction, database reference, and experiment could all be compared, the Materials Project PBE+U reference sat about 0.54 V below measurement: the reference, not the model, dominated the error. A prior-art screen found at least 70% of the targeted Na substitution space already published. We retire the screen, bound what "verified" means for our DFT ledger, and pre-register a calibration audit of it against four benchmark Li couples.
| Subjects: | Materials Science (cond-mat.mtrl-sci); Machine Learning (cs.LG) |
| Cite as: | arXiv:2606.23725 [cond-mat.mtrl-sci] |
| (or arXiv:2606.23725v1 [cond-mat.mtrl-sci] for this version) | |
| https://doi.org/10.48550/arXiv.2606.23725
arXiv-issued DOI via DataCite
|
Full-text links:
Access Paper:
- View PDF
- TeX Source
View a PDF of the paper titled Computational references are not experiments: pre-registered validation of machine-learned sodium-cathode voltages, by Krishna Teja Vepa
Ancillary-file links:
Ancillary files (details):
- BIB_AUDIT.md
- CLAIMS_MAP.md
- D1_PREREGISTRATION_2026-06-10.md
- STEP3B_LITEXP_VERDICT_2026-06-09.md
- STEP3B_OPERATOR_VALIDATION_2026-06-09.md
- bib_verify.py
- build.sh
- consistency_check.py
- curated_na_cathodes.csv
- d1_offset_analysis.py
- fig1_parity.py
- fig2_residuals.py
- fig3_decomposition.py
- gnome_screen_summary.json
- gnome_screen_summary_structuredb.json
- gnome_triage_probe_summary.json
- make_tables.py
- preregistration_2026-06-08.md
- q1_oos_summary.json
- q2_collapse_summary.json
- qme_style.py
- s3b_litexp.py
- s3b_litexp_results.csv
- s3b_litexp_summary.json
- step2_bias_corrected.py
- step2_per_compound.csv
- step2_summary.json
- step3_curation_log.csv
Current browse context:
cond-mat.mtrl-sci
References & Citations
Loading...
Bookmark
Bibliographic Tools
Code, Data, Media
Demos
Related Papers
About arXivLabs
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
IArxiv recommender toggle
IArxiv Recommender
(What is IArxiv?)
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
More from arXiv — Machine Learning
-
Can AI Draw Science? A Benchmark for Evaluating Scientific Figure Generation by Text-to-Image and Multimodal Models
Jun 30
-
On the Necessity of a Liquid Substrate for Mesh Intelligence
Jun 30
-
Position: RL Researchers Need to Distinguish Between Solving Simulators and Using Simulators as a Proxy
Jun 30
-
Learning to Distributedly Estimate under Partially Known Dynamics: A Covariance-Agnostic Neural Kalman Consensus Filter
Jun 30
Discussion (0)
Sign in to join the discussion. Free account, 30 seconds — email code or GitHub.
Sign in →No comments yet. Sign in and be the first to say something.