Pith Number

pith:KA45DUUD

pith:2025:KA45DUUD5US6ROLHJHVFW2ZNS5

not attested not anchored not stored refs resolved

Machine-Learned Interatomic Potential for Predictive Simulation of MoS2 Epitaxy

Emir Bilgili, Nicholas Taormina, Richard Hennig, Simon R. Phillpot, Youping Chen

A machine-learned interatomic potential for MoS2 reproduces defect energies and simulates layered epitaxial growth matching experiments.

arxiv:2512.15952 v3 · 2025-12-17 · cond-mat.mtrl-sci

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Record completeness

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4 Citations open

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Claims

C1strongest claim

Non-equilibrium molecular dynamics simulations reveal layered homoepitaxial growth consistent with experimental observations, demonstrating the formation of van der Waals gaps between successive epilayers and triangular domains bounded by zigzag edges.

C2weakest assumption

The training configurations from DFT sufficiently cover the atomic environments encountered during non-equilibrium epitaxial growth, including edge and defect dynamics not explicitly enumerated in the validation set.

C3one line summary

UF3 MLIP for MoS2 reproduces DFT properties with high fidelity and simulates layered homoepitaxial growth showing van der Waals gaps and zigzag-bounded triangular domains.

References

8 extracted · 8 resolved · 0 Pith anchors

[1] a) The growth rate is ~0.05 ML/ns , the growth temperature is 900K, and the defect density of the triangular domain is ~2.1 1/nm2 2017 · doi:10.1038/natrevmats.2017.33

[2] (5) Dong, J.; Zhang, L.; Dai, X.; Ding, F 2020 · doi:10.1007/s40820-023-01273-5

[3] (6) Aljarb, A.; Fu, J.-H.; Hsu, C.-C.; Chuu, C.-P.; Wan, Y .; Hakami, M.; Naphade, D 2020 · doi:10.1038/s41467-020-19752-3

[4] (28) Wei, Z.; Zhang, C.; Kan, Y .; Zhang, Y .; Chen, Y 2022 · doi:10.1016/j.commatsci.2021.111012

[5] https://doi.org/10.1038/s41524-024-01357-9. (33) Nair, A. K.; Da Silva, C. M.; Amon, C. H. Machine-Learning-Derived Thermal Conductivity of Two-Dimensional TiS2/MoS2 van Der Waals Heterostructures. AP 2024 · doi:10.1038/s41524-024-01357-9

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Receipt and verification

First computed	2026-05-20T02:05:39.088354Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

5039d1d283ed25e8b96749ea5b6b2d97617f7d0adb66f6a2dea8d5837a237ba9

Aliases

arxiv: 2512.15952 · arxiv_version: 2512.15952v3 · doi: 10.48550/arxiv.2512.15952 · pith_short_12: KA45DUUD5US6 · pith_short_16: KA45DUUD5US6ROLH · pith_short_8: KA45DUUD

Agent API

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/KA45DUUD5US6ROLHJHVFW2ZNS5 \
  | 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: 5039d1d283ed25e8b96749ea5b6b2d97617f7d0adb66f6a2dea8d5837a237ba9

Canonical record JSON

{
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    "abstract_canon_sha256": "dc00ccf49da18605ca9d83e5e6439bc8cabdd902717306ddad670fa4f274979d",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cond-mat.mtrl-sci",
    "submitted_at": "2025-12-17T20:26:05Z",
    "title_canon_sha256": "6ff9c0856ea7d3b303e150eefc537b6ba8df97e79529f814112ac1a3d8ea6332"
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    "kind": "arxiv",
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  }
}