Pith Number

pith:VE4HWBM6

pith:2026:VE4HWBM6DCP55BBHCK6LXZXVE5

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SR-CGCNN: Shared Recurrent Convolution in Crystal Graph Neural Networks for Materials Property Prediction

Satadeep Bhattacharjee

Tying convolutional weights across recurrent steps in crystal graph networks lets a three-step model nearly match a three-layer model's accuracy on formation energy and band gap while using only 34.5 percent of the parameters.

arxiv:2605.01304 v2 · 2026-05-02 · cond-mat.mtrl-sci

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3 Author claim open · sign in to claim

4 Citations open

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Claims

C1strongest claim

A three-step SR-CGCNN approaches the accuracy of a standard three-layer CGCNN while using only 34.5% of its trainable convolutional parameters, with formation-energy MAE changing from 0.0945 to 0.0986 eV atom^{-1} and band-gap MAE from 0.4346 to 0.4503 eV.

C2weakest assumption

The assumption that keeping graph construction, pooling, and prediction head identical produces a fair head-to-head comparison between stacked and recurrent message passing; any difference in optimization dynamics or effective receptive field could still favor one architecture.

C3one line summary

SR-CGCNN applies shared weights across recurrent steps in crystal graph convolutions, matching three-layer CGCNN accuracy on Materials Project data with 34.5% of the parameters.

References

19 extracted · 19 resolved · 0 Pith anchors

[1] P. Reiser, M. Neubert, A. Eberhard, L. Torresi, C. Zhou, C. Shao, H. Metni, C. van Hoesel, H. Schopmans, T. Sommer,et al., Communications Materials3, 93 (2022) 2022

[2] V. Fung, J. Zhang, E. Juarez, and B. G. Sumpter, npj Computational Materials7, 84 (2021) 2021

[3] T. Xie and J. C. Grossman, Physical review letters120, 145301 (2018) 2018

[4] C. J. Bartel, A. Trewartha, Q. Wang, A. Dunn, A. Jain, and G. Ceder, npj computational materials6, 97 (2020) 2020

[5] G. Xu, Y. Xue, X. Geng, X. Hou, and J. Xu, Materials Genome Engineering Advances2, e38 (2024) 2024

Receipt and verification

First computed	2026-05-20T00:00:40.214795Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

a9387b059e189fde842712bcbbe6f52773ca788f106e42b0842dd4498a4e4876

Aliases

arxiv: 2605.01304 · arxiv_version: 2605.01304v2 · doi: 10.48550/arxiv.2605.01304 · pith_short_12: VE4HWBM6DCP5 · pith_short_16: VE4HWBM6DCP55BBH · pith_short_8: VE4HWBM6

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/VE4HWBM6DCP55BBHCK6LXZXVE5 \
  | 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: a9387b059e189fde842712bcbbe6f52773ca788f106e42b0842dd4498a4e4876

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "7bdcd6534478818903c4b49227bed2ab4b4aa8f5d7e95eadcc6e87c58fdf1fe5",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cond-mat.mtrl-sci",
    "submitted_at": "2026-05-02T07:26:36Z",
    "title_canon_sha256": "888dadf38172ea909e86c89007aa81eb047a6f01863393f185e113b641f8081a"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.01304",
    "kind": "arxiv",
    "version": 2
  }
}