Pith Number

pith:PDUNZKPR

pith:2026:PDUNZKPRMXNWJKWV3UX3BG2TTR

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Accelerating point defect simulations using data-driven and machine learning approaches

Arun Mannodi-Kanakkithodi, Menglin Huang, Prashun Gorai, Se\'an R. Kavanagh

Data-driven machine learning models trained on DFT calculations accelerate point defect simulations while retaining quantum-mechanical accuracy.

arxiv:2604.21069 v1 · 2026-04-22 · cond-mat.mtrl-sci · physics.chem-ph · physics.comp-ph

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Claims

C1strongest claim

Surrogate models and interatomic potentials trained on density functional theory data lead to predictions with quantum-mechanical accuracies at a fraction of the cost, including for phonon modes and vibrational free energies at finite temperatures.

C2weakest assumption

That the machine learning models trained on limited DFT data can generalize accurately to new defect configurations and materials not seen in training.

C3one line summary

Machine learning models trained on quantum mechanical data can predict defect properties in solids with high accuracy but at much lower computational cost than traditional methods.

References

143 extracted · 143 resolved · 0 Pith anchors

[1] L. Vines, E. Monakhov, and A. Kuznetsov, “Defects in semiconductors,” Journal of Applied Physics, vol. 132, p. 150401, 10 2022 2022

[2] Perspective on defect control in semiconductors for photovoltaics, 2023

[3] The defect challenge of wide-bandgap semiconductors for photovoltaics and beyond, 2022

[4] Pycdt: A python toolkit for modeling point defects in semiconductors and insulators, 2018

[5] Point defect engineering in thin-film solar cells, 2018

Receipt and verification

First computed	2026-05-26T01:03:30.907683Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

78e8dca9f165db64aad5dd2fb09b539c6ab46c6ddccc04b4f0612e1c40456851

Aliases

arxiv: 2604.21069 · arxiv_version: 2604.21069v1 · doi: 10.48550/arxiv.2604.21069 · pith_short_12: PDUNZKPRMXNW · pith_short_16: PDUNZKPRMXNWJKWV · pith_short_8: PDUNZKPR

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/PDUNZKPRMXNWJKWV3UX3BG2TTR \
  | 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: 78e8dca9f165db64aad5dd2fb09b539c6ab46c6ddccc04b4f0612e1c40456851

Canonical record JSON

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    "abstract_canon_sha256": "7ad63f1b6444a7748180f19f0a1b5e0d5b474e8a6978a0f79c6625d5ac45427d",
    "cross_cats_sorted": [
      "physics.chem-ph",
      "physics.comp-ph"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cond-mat.mtrl-sci",
    "submitted_at": "2026-04-22T20:29:41Z",
    "title_canon_sha256": "88d3d6322f350cc1fafe84dfeaff625e264769757c9ed285613fee5c785e4177"
  },
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  "source": {
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    "kind": "arxiv",
    "version": 1
  }
}