Paper Citation Record · LEDGER

Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys

As of 9 August 2026, this Paper Citation Record lists 59 of 59 outbound references and 3 inbound Pith citation observations for arXiv:2502.03578.

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

pith.paper-citation-record.v1
2502.03578 v2

Coverage vector

measured 59 of 59 reference resolution

Typed states for the displayed outbound observations.

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One-hop event checks from named stored sources.

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Pith citing papers itemized under the disclosed page cap.

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A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-06T13:03:41.061625Z

Reference resolution

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External citation measurements

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Outbound references

Observation e8325ca3-1a5c-473b-9af6-fc43a1c035f4 · outbound

This paper cites an unresolved cited work.

Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys Unresolved cited work

Reference 1

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Observation e413e8b2-72fd-4241-8bd8-18b4a30781da · outbound

This paper cites OMat24+MPtrj+sAlex.

Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys OMat24+MPtrj+sAlex

Reference 2

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Observation b268ef32-7cf1-4028-a3b5-0d4b4bf01c12 · outbound

This paper cites Gurnani, S.

Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys Gurnani, S

Reference 3

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This paper cites AlphaMat: A Material Informatics Hub Connecting Data, Features, Models and Applications.

Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys AlphaMat: A Material Informatics Hub Connecting Data, Features, Models and Applications

Reference 4

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This paper cites an unresolved cited work.

Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys Unresolved cited work

Reference 5

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Observation ff2e2d13-9c93-4248-9a0a-5c77144cb7da · outbound

This paper cites Universal Machine Learning Interatomic Potentials are Ready for Phonons.

Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys Universal Machine Learning Interatomic Potentials are Ready for Phonons

Reference 6

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Observation 044ba5b7-9553-4a4f-8a8b-561e96b57c6c · outbound

This paper cites Accelerating High-Throughput Phonon Calculations via Machine Learning Universal Potentials.

Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys Accelerating High-Throughput Phonon Calculations via Machine Learning Universal Potentials

Reference 7

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This paper cites an unresolved cited work.

Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys Unresolved cited work

Reference 8

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Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys Unresolved cited work

Reference 9

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Observation 73e7191e-877d-4a83-93e3-6cb53525b3e6 · outbound

This paper cites Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions.

Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions

Reference 10

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Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys Unresolved cited work

Reference 11

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This paper cites an unresolved cited work.

Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys Unresolved cited work

Reference 12

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Observation 80e26c44-2c56-4646-9508-98bbcb7d6b74 · outbound

This paper cites Goodall, A.S.

Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys Goodall, A.S

Reference 13

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Observation c4ccdc1a-44f2-4368-be83-4885c905b4e9 · outbound

This paper cites Gibson, A.

Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys Gibson, A

Reference 14

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Observation 3fd2f804-248a-47ca-8fd6-27ff5863029c · outbound

This paper cites Xie, J.C.

Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys Xie, J.C

Reference 15

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Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys Unresolved cited work

Reference 16

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Observation 9911ba56-3d8f-449d-9951-32d5e80de8c9 · outbound

This paper cites Choudhary, B.

Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys Choudhary, B

Reference 18

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Observation 378356ea-31e7-4d00-a63e-e9f732e00bb5 · outbound

This paper cites A foundation model for atomistic materials chemistry.

Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys A foundation model for atomistic materials chemistry

Reference 19

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Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys Unresolved cited work

Reference 20

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This paper cites an unresolved cited work.

Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys Unresolved cited work

Reference 21

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Observation 5bd2858f-8e29-4f17-b9a7-1238291b152a · outbound

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Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys Bochkarev, Y

Reference 22

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Observation 70f037c3-5be2-4a3e-bea0-0b883789e419 · outbound

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Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys Merchant, S

Reference 23

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Observation ccfd4749-3338-4b0f-b4c1-6f8c633d4f43 · outbound

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Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys Orb: A Fast, Scalable Neural Network Potential

Reference 24

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Observation 3a3a72e0-e18f-4c9e-8b8f-5c4a2c5c6223 · outbound

This paper cites Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models.

Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models

Reference 25

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This paper cites MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures.

Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures

Reference 26

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Observation b1de9a8a-5d9d-41ef-80f6-ea451c79d85b · outbound

This paper cites Zhang, X.

Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys Zhang, X

Reference 27

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Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

Reference 28

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Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys Unresolved cited work

Reference 29

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Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys Unresolved cited work

Reference 30

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Observation a04011e1-7ea2-4603-bb16-7ea28ec1e0fe · outbound

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Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys Focassio, L.P

Reference 31

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Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys Unresolved cited work

Reference 32

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Observation 44593c75-e1f8-41e1-b9bc-075420c77656 · outbound

This paper cites Perdew, K.

Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys Perdew, K

Reference 33

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Observation 6f686b2a-0070-4af6-b6b9-2cb5867659c9 · outbound

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Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys Kresse, J

Reference 34

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Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys Unresolved cited work

Reference 35

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Observation 126b466a-6293-4710-bbe1-66678f89e5c2 · outbound

This paper cites Blöchl, Projector augmented-wave method, Phys Rev B 50 (1994) 17953–17979.

Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys Blöchl, Projector augmented-wave method, Phys Rev B 50 (1994) 17953–17979

Reference 36

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This paper cites Stukowski, Visualization and analysis of atomistic simulation data with OVITO–the Open Visualization Tool, Model Simul Mat Sci Eng 18 (2010) 015012.

Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys Stukowski, Visualization and analysis of atomistic simulation data with OVITO–the Open Visualization Tool, Model Simul Mat Sci Eng 18 (2010) 015012

Reference 37

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Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys Hjorth Larsen, J

Reference 38

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This paper cites Generalizing Denoising to Non-Equilibrium Structures Improves Equivariant Force Fields.

Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys Generalizing Denoising to Non-Equilibrium Structures Improves Equivariant Force Fields

Reference 39

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Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys Schmidt, T.F.T

Reference 40

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Observation 36976be0-533b-4c2e-9b59-60ec68d08625 · outbound

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Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys Shuang, K

Reference 41

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Observation de015007-2350-42e2-8e2c-3ed28a917a49 · outbound

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Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys Zheng, X.-G

Reference 42

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Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys Sheriff, Y

Reference 43

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Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys Unresolved cited work

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Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys Unresolved cited work

Reference 45

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Observation b6359a01-39c0-42e4-9ba3-635637554de7 · outbound

This paper cites Shuang, Y.

Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys Shuang, Y

Reference 46

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Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys Freitas, Y

Reference 47

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Observation af9e68cd-c5a5-4b0b-9670-292a30caac50 · outbound

This paper cites CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties.

Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties

Reference 48

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Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys Lysogorskiy, A

Reference 49

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Observation 040efea0-3e52-4ed0-9e3b-a3489d7d8a04 · outbound

This paper cites Zhou, R.A.

Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys Zhou, R.A

Reference 50

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Observation 2783b93d-a75c-4f10-aed3-8e48b3ea8ad2 · outbound

This paper cites Chen, S.P.

Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys Chen, S.P

Reference 51

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Observation d1cb6d80-a90c-45d6-aafd-1b91b5c21b29 · outbound

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Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys Himanen, M.O.J

Reference 52

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Observation d432996a-c724-46e1-9b13-03c57d6fe180 · outbound

This paper cites Lopanitsyna, G.

Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys Lopanitsyna, G

Reference 53

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Observation ee97be3a-57f9-4486-a0bb-35d2641352fb · outbound

This paper cites Kang, How graph neural network interatomic potentials extrapolate: Role of the message-passing algorithm, J Chem Phys 161 (2024).

Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys Kang, How graph neural network interatomic potentials extrapolate: Role of the message-passing algorithm, J Chem Phys 161 (2024)

Reference 54

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Observation 8c50d513-c4a2-4f4f-89a5-cc4e80947a36 · outbound

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Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys Unresolved cited work

Reference 55

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Observation f9436c4b-09ee-4d1b-a0df-8cd62f02a175 · outbound

This paper cites Darby, D.P.

Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys Darby, D.P

Reference 56

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Observation 4aa3e679-b0c6-41d7-b976-e2526cfd6aaf · outbound

This paper cites Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations.

Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-09T04:31:33.383394Z

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Observation 82e3ff1a-41e6-41f0-825d-2c9a23ca2660 · outbound

This paper cites Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians.

Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-09T04:31:33.378453Z

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Observation 12233c94-2be6-4532-8b54-de9314e4d310 · outbound

This paper cites Bigi, M.F.

Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys Bigi, M.F

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-09T04:31:33.389489Z

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Observation 044fbbc3-d9c5-472f-b902-0450ba06e80c · outbound

This paper cites an unresolved cited work.

Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys Unresolved cited work

Reference 3572

Resolution: unresolved; no resolver link, observed 2026-08-09T04:31:32.710168Z

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Pith citing papers

Observation e035f326-d1af-4e49-98cd-db376f283cb9 · inbound

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) cites this paper.

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs) Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T05:40:14.271737Z

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Observation 6b5144d0-499b-454d-9265-9d92f2edb92b · inbound

Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications cites this paper.

Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-06T22:23:12.448363Z

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Observation a249f2f3-e661-43ab-8283-63b319dc7c68 · inbound

Heterogeneous Ensemble Enables a Universal Uncertainty Metric for Atomistic Foundation Models cites this paper.

Heterogeneous Ensemble Enables a Universal Uncertainty Metric for Atomistic Foundation Models Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys

Reference 31

Resolution: verified exact; local_arxiv, observed 2026-08-06T13:03:41.230370Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.