Pith Number

pith:GTXE4CSD

pith:2026:GTXE4CSDOYTOOXKGGRPYGWFNF3

not attested not anchored not stored refs pending

Spin-adapted neural network backflow for symmetry-preserving simulations of strongly correlated electrons

Bohan Zhang, Wei-Hai Fang, Yunzhi Li, Zhendong Li, Zibo Wu

A spin-adapted neural network backflow ansatz enforces exact spin symmetry in variational wavefunctions for strongly correlated electrons.

arxiv:2604.06841 v2 · 2026-04-08 · physics.chem-ph · cond-mat.str-el

Open paper page JSON Open Graph Bundle Merged state Verified badge What is a Pith Number?

Add to your LaTeX paper

\usepackage{pith}
\pithnumber{GTXE4CSDOYTOOXKGGRPYGWFNF3}

Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge

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

Applications to prototypical strongly correlated molecules demonstrate that SA-NNBF consistently outperforms standard NNBF with a similar number of parameters. Furthermore, it surpasses the accuracy of the state-of-the-art spin-adapted density matrix renormalization group (SA-DMRG) algorithm for FeMoco with a significantly reduced computational resource.

C2weakest assumption

That the introduced tensor compression algorithm for spin eigenfunctions and the particle-hole duality representation preserve both the exact spin symmetry and the variational accuracy of the full wavefunction when applied to systems with more than one hundred electrons.

C3one line summary

SA-NNBF creates fully antisymmetric, spin-symmetric neural wavefunctions via sum-of-products spin eigenfunctions and tensor compression, enabling VMC calculations that outperform standard NNBF and SA-DMRG on systems with over 100 electrons including FeMoco.

Receipt and verification

First computed	2026-06-11T01:09:34.687958Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

34ee4e0a437626e75d46345f8358ad2efc13118ce6d05726283f9ddaf8931178

Aliases

arxiv: 2604.06841 · arxiv_version: 2604.06841v2 · doi: 10.48550/arxiv.2604.06841 · pith_short_12: GTXE4CSDOYTO · pith_short_16: GTXE4CSDOYTOOXKG · pith_short_8: GTXE4CSD

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/GTXE4CSDOYTOOXKGGRPYGWFNF3 \
  | 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: 34ee4e0a437626e75d46345f8358ad2efc13118ce6d05726283f9ddaf8931178

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "dfabb11727ae799792a70940aa1a490c3c9a9b7c2eae785b92e757f6354a3977",
    "cross_cats_sorted": [
      "cond-mat.str-el"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "physics.chem-ph",
    "submitted_at": "2026-04-08T09:02:49Z",
    "title_canon_sha256": "5eb4f7ff0142a09c3030b330b4c4653c2a6933dc82ba2e88662b4889948735d4"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2604.06841",
    "kind": "arxiv",
    "version": 2
  }
}