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pith:7R5FTE2L

pith:2024:7R5FTE2LEAPBD7XL7N33OGIGGY

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Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models

Ammar Rizvi, Brandon M. Wood, C. Lawrence Zitnick, Luis Barroso-Luque, Meng Gao, Misko Dzamba, Muhammed Shuaibi, Xiang Fu, Zachary W. Ulissi

Open Materials 2024 supplies 110 million DFT calculations and EquiformerV2 models that reach F1 scores above 0.9 for ground-state stability.

arxiv:2410.12771 v1 · 2024-10-16 · cond-mat.mtrl-sci · cs.AI · physics.comp-ph

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Claims

C1strongest claim

Our EquiformerV2 models achieve state-of-the-art performance on the Matbench Discovery leaderboard and are capable of predicting ground-state stability and formation energies to an F1 score above 0.9 and an accuracy of 20 meV/atom, respectively.

C2weakest assumption

The DFT calculations in the dataset provide sufficiently accurate representations of real material ground-state stabilities and formation energies, and the models trained on this data will generalize reliably to new, unseen materials.

C3one line summary

OMat24 provides over 110 million DFT calculations and EquiformerV2 models that reach state-of-the-art performance on material stability and formation energy prediction.

References

65 extracted · 65 resolved · 1 Pith anchors

[1] Lawrence Zitnick, and Zachary Ulissi 2020

[2] An introduction to electrocatalyst design using machine learning for renewable energy storage 2010

[3] Robust and synthesizable photocatalysts for co2 reduction: a data-driven materials discovery.Nature Communications, 10(1):443, 2019 2019

[4] Brabson, Abhishek Das, Zachary Ulissi, Matt Uyttendaele, Andrew J 2023

[5] Recent advances and applications of deep learning methods in materials science.npj Computational Materials, 8(1):59, 2022 2022

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Cited by

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Receipt and verification

First computed	2026-05-17T23:38:46.205046Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

fc7a59934b201e11feebfb77b719063615971e294c9fb492043cfca7f00c08ba

Aliases

arxiv: 2410.12771 · arxiv_version: 2410.12771v1 · doi: 10.48550/arxiv.2410.12771 · pith_short_12: 7R5FTE2LEAPB · pith_short_16: 7R5FTE2LEAPBD7XL · pith_short_8: 7R5FTE2L

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/7R5FTE2LEAPBD7XL7N33OGIGGY \
  | 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: fc7a59934b201e11feebfb77b719063615971e294c9fb492043cfca7f00c08ba

Canonical record JSON

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    "primary_cat": "cond-mat.mtrl-sci",
    "submitted_at": "2024-10-16T17:48:34Z",
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