Pith Number
pith:MT5ZQMS2
pith:2025:MT5ZQMS2TFQKBU6FHRJ22ICPLM
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not anchored
not stored
refs pending
Integrating Density Functional Theory with Deep Neural Networks for Accurate Voltage Prediction in Alkali-Metal-Ion Battery Materials
arxiv:2503.13067 v3 · 2025-03-17 · cond-mat.mtrl-sci · physics.chem-ph
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{MT5ZQMS2TFQKBU6FHRJ22ICPLM}
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Record completeness
1
Bitcoin timestamp
2
Internet Archive
3
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4
Citations
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Replications
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Portable graph bundle live · download bundle · merged
state
The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same
current state with the deterministic merge algorithm.
Receipt and verification
| First computed | 2026-07-05T11:56:53.885086Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
64fb98325a9960a0d3c53c53ad204f5b3bb2dacc265ed1d857c69fce616aed19
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/MT5ZQMS2TFQKBU6FHRJ22ICPLM \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 64fb98325a9960a0d3c53c53ad204f5b3bb2dacc265ed1d857c69fce616aed19
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "2082a522489b807f2296f1579fd26d5d08b686aa86fd00add0800700a853e08e",
"cross_cats_sorted": [
"physics.chem-ph"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"primary_cat": "cond-mat.mtrl-sci",
"submitted_at": "2025-03-17T11:15:31Z",
"title_canon_sha256": "98384dbcfe1b23ffe42924001bbec5f5b570e22da67aa95f50b520fb974bf39d"
},
"schema_version": "1.0",
"source": {
"id": "2503.13067",
"kind": "arxiv",
"version": 3
}
}