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pith:6FEWOBSB

pith:2025:6FEWOBSBS3BDRKUU5URVNL4UET

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Beyond Diamond: Interpretable Machine Learning Reveals Design Principles for Quantum Defect Host Materials

Mohammed Mahshook, Rudra Banerjee

Machine learning on compositions alone extracts consensus design rules to identify 122 high-confidence quantum defect host candidates.

arxiv:2506.03844 v3 · 2025-06-04 · cond-mat.mtrl-sci

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Claims

C1strongest claim

By contrasting feature attributions across seven diverse classifiers in a heterogeneous Rashomon set, the framework extracts consensus design rules (filled valence s-, d-, and f-shells, low chemical heterogeneity, enrichment in C, S, Si, O) that enable identification of 122 high-confidence candidates from ~45,000 compounds, with DFT validation showing dielectric screening as a coherence proxy (R² = 0.89 against experimental T₂) and favorable mid-gap states in TiO₂.

C2weakest assumption

That consensus feature attributions from composition-only classifiers trained on existing data capture the essential physical requirements for quantum defect hosting and that the dielectric-T₂ correlation observed in 12 materials will generalize to the full set of 122 screened candidates without structural or defect-specific details in the initial filter.

C3one line summary

A composition-only ML framework with Rashomon ensembles extracts consensus design rules and screens ~45,000 compounds to identify 122 high-confidence quantum defect hosts, recovering known materials and predicting new ones validated by limited DFT.

References

61 extracted · 61 resolved · 0 Pith anchors

[1] Building a quantum- ready ecosystem 2024

[2] Review of distributed quantum computing: from sin- gle qpu to high performance quantum computing 2025

[3] Scalable entanglement certi- fication via quantum communication 2024

[4] Quantum sensing and metrology for fundamental physics with molecules 2024

[5] An elementary review on basic principles and developments of qubits for quantum computing 2024

Receipt and verification

First computed	2026-05-20T00:04:10.316279Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

f14967064196c238aa94ed2356af9424ed950e03b55c70864f482d9ecee560d7

Aliases

arxiv: 2506.03844 · arxiv_version: 2506.03844v3 · doi: 10.48550/arxiv.2506.03844 · pith_short_12: 6FEWOBSBS3BD · pith_short_16: 6FEWOBSBS3BDRKUU · pith_short_8: 6FEWOBSB

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/6FEWOBSBS3BDRKUU5URVNL4UET \
  | 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: f14967064196c238aa94ed2356af9424ed950e03b55c70864f482d9ecee560d7

Canonical record JSON

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    "abstract_canon_sha256": "4e8a3da66193987d96671a8a815d2f3150586488446e2446b44a81e4fefb53d5",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by-sa/4.0/",
    "primary_cat": "cond-mat.mtrl-sci",
    "submitted_at": "2025-06-04T11:21:17Z",
    "title_canon_sha256": "1eae3a34febf1a37233cde1aad626949828b00118a8682da53957f03f2f13eee"
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