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pith:3DZXNGPV

pith:2026:3DZXNGPVBX5U2PTADYKNJYDB6K

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Force-Aware Neural Tangent Kernels for Scalable and Robust Active Learning of MLIPs

Eszter Varga-Umbrich, Jules Tilly, Olivier Peltre, Paul Duckworth, Shikha Surana, Zachary Weller-Davies

Mixed parameter-coordinate derivatives of the NTK yield natural similarity metrics for force-aware active learning in MLIPs.

arxiv:2605.13788 v1 · 2026-05-13 · cs.LG

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Claims

C1strongest claim

we demonstrate the effectiveness of the joint energy-force NTK on the OC20 dataset, where force-aware acquisition is crucial: it achieves the lowest energy and force MAE and RMSE across all metrics and distribution splits.

C2weakest assumption

The mixed parameter-coordinate derivatives of the NTK yield effective natural similarity metrics for vector-field force prediction in pretrained MLIPs without requiring additional fitting or validation.

C3one line summary

Force-aware NTKs and chunked acquisition enable scalable, robust active learning for MLIPs, achieving lowest energy and force errors on OC20 and remaining competitive on other benchmarks.

References

123 extracted · 123 resolved · 1 Pith anchors

[1] 2024 , journal = 2025 · doi:10.1021/acs.chemrev.4c00572

[2] On representing chemical environments 2013 · doi:10.1103/physrevb.87.184115

[3] \ Elena , author Dávid P 2023

[4] Ilyes Batatia, Dávid Péter Kovács, Gregor N. C. Simm, Christoph Ortner, and Gábor Csányi. Mace: Higher order equivariant message passing neural networks for fast and accurate force fields, 2023 b . UR 2023

[5] E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials 2022 · doi:10.1038/s41467-022-29939-5

Receipt and verification

First computed	2026-05-18T02:44:15.659131Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

d8f37699f50dfb4d3e601e14d4e061f2afce368400fe2a08ba13d37718a5aa32

Aliases

arxiv: 2605.13788 · arxiv_version: 2605.13788v1 · doi: 10.48550/arxiv.2605.13788 · pith_short_12: 3DZXNGPVBX5U · pith_short_16: 3DZXNGPVBX5U2PTA · pith_short_8: 3DZXNGPV

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/3DZXNGPVBX5U2PTADYKNJYDB6K \
  | 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: d8f37699f50dfb4d3e601e14d4e061f2afce368400fe2a08ba13d37718a5aa32

Canonical record JSON

{
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    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-05-13T17:08:37Z",
    "title_canon_sha256": "b40f1f5dac0d7cc0955a7c214b07e75a58db8a8ce7f0a0de373987cd8a6396e5"
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    "kind": "arxiv",
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