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Paper Citation Record · LEDGER

STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces

As of 14 August 2026, this Paper Citation Record lists 64 of 64 outbound references and 0 inbound Pith citation observations for arXiv:2607.17129.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2607.17129 v1

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measured 64 of 64 reference resolution

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64 of 64 outbound references displayed

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Outbound references

Observation a636e18f-990e-464c-a9b1-e107cf79f886 · outbound

This paper cites an unresolved cited work.

STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces Unresolved cited work

Reference 1

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STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces Unresolved cited work

Reference 2

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STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces Unresolved cited work

Reference 3

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STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces Unresolved cited work

Reference 4

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Observation 7c27cf4f-7bcd-441a-8c0d-c6ffe79a25fc · outbound

This paper cites To explicitly consider the effect of LSF, we performed generalized Langevin spin dynam- ics simulations [41].

STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces To explicitly consider the effect of LSF, we performed generalized Langevin spin dynam- ics simulations [41]

Reference 5

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Observation 12e0276e-d1e6-4599-a55e-32b91fcd2ac9 · outbound

This paper cites The in-plane lattice constant was 6.9685 ˚A.

STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces The in-plane lattice constant was 6.9685 ˚A

Reference 6

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This paper cites Longitudinal fluctuations of local magnetic moments were included using the generalized Langevin spin-dynamics formalism.

STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces Longitudinal fluctuations of local magnetic moments were included using the generalized Langevin spin-dynamics formalism

Reference 7

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Observation f27d1560-7326-4e0f-a0fa-b1a28193cdab · outbound

This paper cites Behler and M.

STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces Behler and M

Reference 8

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Observation bed84f0b-8e00-4d8c-b655-d706a52a6cff · outbound

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STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces Unresolved cited work

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This paper cites Zhang, J.

STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces Zhang, J

Reference 10

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Observation 53ae7a27-32f4-4b56-bfaa-a3e042b255cb · outbound

This paper cites Batzner, A.

STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces Batzner, A

Reference 11

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Observation d28aadcc-cefe-442c-a7ab-bb0c92ad13a5 · outbound

This paper cites Batatia, D.

STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces Batatia, D

Reference 12

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Observation 2c88005b-ebed-4155-92ee-6c7aea49fbeb · outbound

This paper cites an unresolved cited work.

STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces Unresolved cited work

Reference 13

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Observation 4e770c44-29c2-4a29-be1f-c90a02539701 · outbound

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STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces Unresolved cited work

Reference 14

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Observation ec648df7-1917-430b-94ae-7f543740a3e4 · outbound

This paper cites Novikov, B.

STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces Novikov, B

Reference 15

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This paper cites Drautz, Atomic cluster expansion of scalar, vectorial, and tensorial properties including magnetism and charge transfer, Physical Review B102, 024104 (2020).

STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces Drautz, Atomic cluster expansion of scalar, vectorial, and tensorial properties including magnetism and charge transfer, Physical Review B102, 024104 (2020)

Reference 16

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Observation 6e90fded-11d3-4a52-9be7-4c1a7b7173b9 · outbound

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STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces Unresolved cited work

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Observation 38cb675a-d07b-4a9b-a9ad-e12f913c4a7a · outbound

This paper cites Equivariant Many-body Message Passing Interatomic Potentials for Magnetic Materials.

STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces Equivariant Many-body Message Passing Interatomic Potentials for Magnetic Materials

Reference 18

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STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces Unresolved cited work

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STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces e3nn: Euclidean Neural Networks

Reference 20

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STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces Unresolved cited work

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STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces Unresolved cited work

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STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces Zheng, X

Reference 23

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STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces Unresolved cited work

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This paper cites Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds.

STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds

Reference 25

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STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces Weiler, M

Reference 26

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STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces Unresolved cited work

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STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces Adam: A Method for Stochastic Optimization

Reference 28

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STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces On the Convergence of Adam and Beyond

Reference 29

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STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces Unresolved cited work

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STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces Unresolved cited work

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STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces Imbalzano, A

Reference 32

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STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces Xu, (2026), private communication

Reference 33

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STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces Pajda, J

Reference 34

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STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces Buczek, A

Reference 35

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STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces Unresolved cited work

Reference 36

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STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces Scaling Laws for Neural Language Models

Reference 37

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STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces Dillon Jr and C

Reference 38

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STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces Huang, G

Reference 39

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Observation 6bdef052-6c73-497f-9f79-754f5ec852ac · outbound

This paper cites Sarkar and P.

STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces Sarkar and P

Reference 40

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Observation c128f415-9d5e-495a-8887-182ebcfb7c5b · outbound

This paper cites Chen, J.-H.

STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces Chen, J.-H

Reference 41

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Observation efdf2c30-ca25-4178-a7b3-8a7fc8e4cf18 · outbound

This paper cites Bertrand and H.

STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces Bertrand and H

Reference 42

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source=pdf_text observed=2026-08-01T19:00:24.186634Z digest=sha256:6361a9d519931345e848c5b5fec64305bbee505abe96c02bcbdfce6818ddb1ae

Observation c4054a24-cfc3-4a70-9513-81f42396f316 · outbound

This paper cites McAlister and P.

STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces McAlister and P

Reference 43

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source=pdf_text observed=2026-08-01T19:00:24.303408Z digest=sha256:ca95579b5aae107a851ae1911d5d27d9d27e7dd310d82468e5965ebca2a2e00f

Observation fa39dd4e-1894-4f14-8a2b-c556e58b7c24 · outbound

This paper cites Bao, Z.-L.

STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces Bao, Z.-L

Reference 44

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source=pdf_text observed=2026-08-01T19:00:24.416591Z digest=sha256:354856c989825949d61e5d130725956fcc9d0a9cac0e9323e3dc8675b2ca144c

Observation cc7bd561-5a25-4a72-9dc9-6d1e2d89118b · outbound

This paper cites an unresolved cited work.

STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces Unresolved cited work

Reference 45

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source=pdf_text observed=2026-08-01T19:00:24.526290Z digest=sha256:e86f3fecf8d64623a0bbffc4b6f285d09ecb5b3767cb6399e55e0061fc54866e

Observation 1413290a-fc64-409f-b062-527e9a83198c · outbound

This paper cites Huang, G.

STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces Huang, G

Reference 46

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source=pdf_text observed=2026-08-01T19:00:24.594799Z digest=sha256:40fd4e2d2d4ee9892309c946c77a9ee83d06f8f1114283f500394c84fadfebda

Observation a9966496-c854-4e64-ac28-43dc53d4c11c · outbound

This paper cites Rzepkowski, M.

STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces Rzepkowski, M

Reference 47

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source=pdf_text observed=2026-08-01T19:00:24.660550Z digest=sha256:27d1b723afd19fa6d2cd56033ef8bc51e43f4c31ec0e5c1c1ea10a69d05de18d

Observation 8c801b4d-e541-4fcf-b2c5-6296c2b45e2a · outbound

This paper cites Ma and S.

STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces Ma and S

Reference 48

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source=pdf_text observed=2026-08-01T19:00:24.745320Z digest=sha256:82e8bd132273c7358b78e6682d0811ecf379c1549d16cb1b4fd07381d1c4c23d

Observation 38b7914e-19e8-43f9-b325-b95ee2daebb7 · outbound

This paper cites Crangle and G.

STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces Crangle and G

Reference 49

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source=pdf_text observed=2026-08-01T19:00:24.798218Z digest=sha256:30e41d29fb9a3c2e4896a4ce4c5d74cbb45ff16bd2dbff2a25e245fc16e2d8fa

Observation c6bcf887-23b6-4312-9941-b950a1b19979 · outbound

This paper cites Kresse and J.

STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces Kresse and J

Reference 50

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source=pdf_text observed=2026-08-01T19:00:24.871323Z digest=sha256:93b84110e0601e9743f7b51c592c12431de62d5b08a42085e6d66ab5e158fa34

Observation b42f8970-2572-4616-bd7f-f9bfcf74f1c7 · outbound

This paper cites Kresse and J.

STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces Kresse and J

Reference 51

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source=pdf_text observed=2026-08-01T19:00:24.973369Z digest=sha256:b2db560280b276d075af3d3176446c47b366a34483bcae6e0424bb80ade342b9

Observation eef06957-4aad-4e64-8a1f-b6f70adf21b4 · outbound

This paper cites an unresolved cited work.

STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces Unresolved cited work

Reference 52

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source=pdf_text observed=2026-08-01T19:00:25.055619Z digest=sha256:50f5d0774a9b48b5dfbefe96e5698c3dad42c9c8e3a8d9cfcc57562ca63db719

Observation a71da8bb-ce5a-4611-be09-44a9aec74498 · outbound

This paper cites Kresse and D.

STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces Kresse and D

Reference 53

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source=pdf_text observed=2026-08-01T19:00:25.125213Z digest=sha256:fb2bccfbc4bab39bde1e101f6410d7255ef4c8c336c930420b5062957c3e815c

Observation 68cd3579-943a-4711-8e85-7c3a489c92b9 · outbound

This paper cites an unresolved cited work.

STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces Unresolved cited work

Reference 54

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source=pdf_text observed=2026-08-01T19:00:25.195152Z digest=sha256:4e23619a1f04a7b92bc9c55f1528eec5984f6eeeb3dbdebfce1593e56b999518

Observation 8b97c759-c315-476f-959f-c1456ac99ec7 · outbound

This paper cites Grimme, S.

STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces Grimme, S

Reference 55

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source=pdf_text observed=2026-08-01T19:00:25.278414Z digest=sha256:07e0cee322c6cf1c7cdd9dd0c7142aa963bdab1adabac1ac6fda1fb1053d4afe

Observation 9aa01770-e18e-468e-9e78-40594f3d2c3a · outbound

This paper cites an unresolved cited work.

STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces Unresolved cited work

Reference 56

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source=pdf_text observed=2026-08-01T19:00:25.330600Z digest=sha256:d5779bb3aebfd8f18a48252da17761b1c868d759b8314ec3a89492db2fcdbfcf

Observation d4275068-8896-42b5-be3c-3657454b1a73 · outbound

This paper cites Cococcioni and S.

STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces Cococcioni and S

Reference 57

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source=pdf_text observed=2026-08-01T19:00:25.406228Z digest=sha256:b07c7dcee871c666fbf5cba1317fbf74f43b702dabbf85dedf1e0df13ba93f15

Observation c84cc9b7-3837-4ae9-8164-b907cb02c9f8 · outbound

This paper cites an unresolved cited work.

STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces Unresolved cited work

Reference 58

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source=pdf_text observed=2026-08-01T19:00:25.507935Z digest=sha256:8cec0260a1f804d7b72e7635ebcd44e1cf1554cfb577e9df975c762798cf4c7e

Observation 92165f85-7d1a-46c6-9faa-d426e088b4bb · outbound

This paper cites an unresolved cited work.

STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces Unresolved cited work

Reference 59

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source=pdf_text observed=2026-08-01T19:00:25.608266Z digest=sha256:bbe68655450b821f011177b55c97f22bc96d27a1694eb46b83be9a32b28aec46

Observation f01533ae-1806-46b4-af68-bc112b4f6037 · outbound

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STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces Unresolved cited work

Reference 60

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source=pdf_text observed=2026-08-01T19:00:25.682639Z digest=sha256:816dd16743b9a080ae160cf8b969df01243cc4042dba74431c4b69168984b91e

Observation c26fb003-13c5-46dc-8262-ad04b046ab6c · outbound

This paper cites an unresolved cited work.

STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces Unresolved cited work

Reference 61

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source=pdf_text observed=2026-08-01T19:00:25.744649Z digest=sha256:ed39a2b29674e355b5d1fc6af6704e2e643e028616c536e6248f2944240a3041

Observation 5fd1de45-aa0e-4591-87dc-a69b3c75c60a · outbound

This paper cites Chang, Y.

STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces Chang, Y

Reference 62

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source=pdf_text observed=2026-08-01T19:00:25.805158Z digest=sha256:8e28c10f6e2992e2d556132dbdb960f88fc9581c113e3042fcababe5c4a9b70c

Observation 3d8349ab-35d9-4b52-b3cd-4aef9a5693ea · outbound

This paper cites an unresolved cited work.

STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces Unresolved cited work

Reference 63

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source=pdf_text observed=2026-08-01T19:00:25.901513Z digest=sha256:a2864955d68cb4ed44a79a224c8d5ffaa36b55543e162d1be2f65215c3e24659

Observation c2c351f3-057d-4a97-a829-a1c8270a2109 · outbound

This paper cites an unresolved cited work.

STEP: Spin Tensor Equivariant Potential for Data-Efficient Learning of Magnetic Potential Energy Surfaces Unresolved cited work

Reference 64

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source=pdf_text observed=2026-08-01T19:00:25.968351Z digest=sha256:cf39ca8b7bbdb00a35e21060d07ec897aafab22f5ce6028abbf72ad937a676e8

Pith citing papers

No inbound Pith citation observations are available.