Pith Number

pith:BGM3WENS

pith:2025:BGM3WENSV54UJG7YW2YER2K6VF

not attested not anchored not stored refs pending

Learning Thermoelectric Transport from Crystal Structures via Multiscale Graph Neural Network

Fang Lyu, Jing Shi, Ling Miao, Tan Peng, Wei Cao, Wenhao Xie, Yijing Zuo, Yue Hou, Yuxuan Zeng, Ziyu Wang

A multiscale graph neural network estimates electronic transport coefficients in thermoelectric crystals directly from their structures.

arxiv:2512.06697 v3 · 2025-12-07 · cond-mat.mtrl-sci · physics.app-ph

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\usepackage{pith}
\pithnumber{BGM3WENSV54UJG7YW2YER2K6VF}

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Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ 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.

Claims

C1strongest claim

The model achieves state-of-the-art performance on benchmark datasets with remarkable extrapolative capability. By combining the proposed GNN with ab initio calculations, we successfully identify compounds exhibiting outstanding electronic transport properties.

C2weakest assumption

That encoding crystal structures and physicochemical properties in a multiscale manner (global, atomic, bond, and angular levels) is sufficient to capture the underlying physics governing electronic transport coefficients.

C3one line summary

A multiscale GNN predicts thermoelectric transport properties from crystal structures, achieves SOTA performance, and identifies promising new compounds via combination with ab initio calculations.

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-06-08T01:05:05.204180Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

0999bb11b2af79449bf8b6b048e95ea97d5922867b9b44e97010d05866022950

Aliases

arxiv: 2512.06697 · arxiv_version: 2512.06697v3 · doi: 10.48550/arxiv.2512.06697 · pith_short_12: BGM3WENSV54U · pith_short_16: BGM3WENSV54UJG7Y · pith_short_8: BGM3WENS

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/BGM3WENSV54UJG7YW2YER2K6VF \
  | 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: 0999bb11b2af79449bf8b6b048e95ea97d5922867b9b44e97010d05866022950

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "53226320e685d26f51a6ddb31e29acf418b6a72a4379c4d73142042a90a015d8",
    "cross_cats_sorted": [
      "physics.app-ph"
    ],
    "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
    "primary_cat": "cond-mat.mtrl-sci",
    "submitted_at": "2025-12-07T07:21:05Z",
    "title_canon_sha256": "2b9ff15a1efc7f70df86658a720306d4e0107e88701e3fca3b54539467b7b5f8"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2512.06697",
    "kind": "arxiv",
    "version": 3
  }
}