Paper Citation Record · LEDGER

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties

As of 23 August 2026, this Paper Citation Record lists 100 of 124 outbound references and 3 inbound Pith citation observations for arXiv:2412.10516.

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

pith.paper-citation-record.v1
2412.10516 v4

Coverage vector

measured 100 of 124 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T15:58:20.000421Z

measured 103 of 103 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T04:31:33.295572Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T02:28:24.338817Z

Reference resolution

100 of 124 outbound references displayed

verified exact2
verified fuzzy26
unresolved72
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

1
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation d1f84075-e3da-4fdc-b214-e9f2096bda82 · outbound

This paper cites B.; Miller, R.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties B.; Miller, R

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.717124Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c1f7bd2c-188d-4ef8-bdda-5ba25bfd7331 · outbound

This paper cites an unresolved cited work.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties Unresolved cited work

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.721196Z

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Observation f0b3e742-f94d-4473-b816-fee6273860d5 · outbound

This paper cites an unresolved cited work.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties Unresolved cited work

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.723999Z

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Observation 2c010d0c-907d-4dec-a0eb-e672a0ec442c · outbound

This paper cites P.; Tildesley, D.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties P.; Tildesley, D

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.726768Z

Source-reported events for the cited work

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Observation 30ffe703-5aa5-41a0-8f05-9377e049b183 · outbound

This paper cites M.; Klimeck, G.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties M.; Klimeck, G

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.729624Z

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Observation 6ba1da60-b274-44b0-b04d-a24b56e8c54c · outbound

This paper cites T.; Oviedo, F.; Canepa, P.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties T.; Oviedo, F.; Canepa, P

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.732342Z

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Observation 2dad442d-6957-4583-9821-84ee12a090a8 · outbound

This paper cites E.; Scourtas, A.; Schmidt, K.; Price-Skelly, O.; Engler, W.; Foster, I.; Blaiszik, B.; Voyles, P.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties E.; Scourtas, A.; Schmidt, K.; Price-Skelly, O.; Engler, W.; Foster, I.; Blaiszik, B.; Voyles, P

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.736161Z

Source-reported events for the cited work

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Observation 7c03c5ae-1328-44f9-918a-31f590792ea8 · outbound

This paper cites an unresolved cited work.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties Unresolved cited work

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.738831Z

Source-reported events for the cited work

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Observation 2c71fd48-babc-43fc-812f-70ca29b355e4 · outbound

This paper cites W.; Choudhary, A.; Agrawal, A.; Billinge, S.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties W.; Choudhary, A.; Agrawal, A.; Billinge, S

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.741473Z

Source-reported events for the cited work

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Observation 5a867efe-2ca0-46a2-bdbc-d18095acaf0c · outbound

This paper cites AI-driven inverse design of materials: Past, present and future.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties AI-driven inverse design of materials: Past, present and future

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.744156Z

Source-reported events for the cited work

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Observation 0c89cbda-90e2-46e7-90b6-e1ee8a487d9d · outbound

This paper cites Atomgpt: Atomistic generative pretrained transformer for forward and inverse materials design.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties Atomgpt: Atomistic generative pretrained transformer for forward and inverse materials design

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.747153Z

Source-reported events for the cited work

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Observation 8b35f669-ca2f-4103-8796-56f915680660 · outbound

This paper cites an unresolved cited work.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties Unresolved cited work

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.750428Z

Source-reported events for the cited work

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Observation 91771c92-4b4e-4b7c-b287-477f0b7ad8fa · outbound

This paper cites L.; Van de Walle, C.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties L.; Van de Walle, C

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.752881Z

Source-reported events for the cited work

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Observation de1d3e80-06fe-449d-af7a-7957e155fdaf · outbound

This paper cites Equilibrium point defect and charge carrier concentrations in a material determined through calculation of the self-consistent Fermi energy.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties Equilibrium point defect and charge carrier concentrations in a material determined through calculation of the self-consistent Fermi energy

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.755533Z

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Observation 3ccd197d-daaf-40bc-91db-2aeba3df8bdf · outbound

This paper cites an unresolved cited work.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties Unresolved cited work

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.758226Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e62ff4eb-ab57-42e8-af30-2ebafaaadf05 · outbound

This paper cites H.; Gollapalli, P.; Manganaris, P.; Yadav, S.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties H.; Gollapalli, P.; Manganaris, P.; Yadav, S

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.761495Z

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Observation 250d30c4-a0a5-4c69-9868-337afbf7c697 · outbound

This paper cites E.; Alkauskas, A.; Engel, M.; Kresse, G.; Wickramaratne, D.; Shen, J.-X.; Dreyer, C.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties E.; Alkauskas, A.; Engel, M.; Kresse, G.; Wickramaratne, D.; Shen, J.-X.; Dreyer, C

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.765167Z

Source-reported events for the cited work

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Observation 81ab667e-65c8-49bf-9009-7c100e8d7b68 · outbound

This paper cites an unresolved cited work.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties Unresolved cited work

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.767787Z

Source-reported events for the cited work

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Observation 5cbd1c84-904a-4ded-85b2-378a21ba299b · outbound

This paper cites Density functional descriptions of interfacial electronic structure.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties Density functional descriptions of interfacial electronic structure

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.770355Z

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Unavailable: canonical work link unavailable.

Observation bc21be6f-7261-4627-a08e-21bb10dffa51 · outbound

This paper cites an unresolved cited work.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties Unresolved cited work

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.773052Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3388327c-3ba0-4dad-91e6-5849f3f20da3 · outbound

This paper cites T.; Walsh, A.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties T.; Walsh, A

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.775957Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a464d943-2a08-4109-89ff-5caa8dc488b6 · outbound

This paper cites T.; Sai Gautam, G.; Canepa, P.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties T.; Sai Gautam, G.; Canepa, P

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.778813Z

Source-reported events for the cited work

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Observation 86930c0e-1e7f-4bb5-9ff6-f30056829f5d · outbound

This paper cites Band alignment of semiconductors from density-functional theory and many-body perturbation theory.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties Band alignment of semiconductors from density-functional theory and many-body perturbation theory

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.781310Z

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Observation 0c4bcbd9-f620-4cac-a461-921ad1f0d9a6 · outbound

This paper cites A.; Ong, S.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties A.; Ong, S

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.784006Z

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Unavailable: canonical work link unavailable.

Observation 6c2b1d96-d251-4748-a085-db74eab9773a · outbound

This paper cites an unresolved cited work.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties Unresolved cited work

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.786745Z

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Observation 34479537-a838-4ecb-b122-7637ad339659 · outbound

This paper cites an unresolved cited work.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties Unresolved cited work

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.790808Z

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Unavailable: canonical work link unavailable.

Observation 68961dd2-a0c1-406e-bb8c-781f8879d16f · outbound

This paper cites L.; Bernstein, N.; Bart \'o k, A.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties L.; Bernstein, N.; Bart \'o k, A

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.793376Z

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Observation 289b494e-3c38-48e9-b256-5c23fcd4781a · outbound

This paper cites The ab initio amorphous materials database: Empowering machine learning to decode diffusivity.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties The ab initio amorphous materials database: Empowering machine learning to decode diffusivity

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-08-11T15:58:20.206518Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e9b51008-0c57-4ac0-b5d0-4166888dd7db · outbound

This paper cites an unresolved cited work.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties Unresolved cited work

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.798906Z

Source-reported events for the cited work

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Observation 85f20375-012a-43ec-ba5d-b217090ec583 · outbound

This paper cites C.; Dongale, T.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties C.; Dongale, T

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.801705Z

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Observation 3b9ee2a5-c999-458d-95f0-014ff83702ba · outbound

This paper cites K.; Casewit, C.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties K.; Casewit, C

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.805318Z

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Observation 6adc80ef-6450-4d49-9f61-1a1b72fb7896 · outbound

This paper cites Generalized neural-network representation of high-dimensional potential-energy surfaces.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties Generalized neural-network representation of high-dimensional potential-energy surfaces

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.807663Z

Source-reported events for the cited work

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Observation 48fe4085-858d-464d-accf-cbea8d190264 · outbound

This paper cites P.; Payne, M.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties P.; Payne, M

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.812443Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b1184eac-d666-465f-b946-548637108439 · outbound

This paper cites A.; Thompson, A.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties A.; Thompson, A

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.815166Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3cbeb7dd-adb5-436c-ac73-32f5ac959ea4 · outbound

This paper cites J.; Kornbluth, M.; Kozinsky, B.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties J.; Kornbluth, M.; Kozinsky, B

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.818060Z

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Observation a7ce3b1b-0bd6-482c-976d-458e3b09247e · outbound

This paper cites P.; Hautier, G.; Chen, W.; Richards, W.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties P.; Hautier, G.; Chen, W.; Richards, W

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.820803Z

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Observation a88921fb-5fe6-46ad-964d-c586322dc55e · outbound

This paper cites F.; DeCost, B.; Biacchi, A.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties F.; DeCost, B.; Biacchi, A

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.824340Z

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Observation 44737918-1a34-46c0-b479-e948bf498794 · outbound

This paper cites an unresolved cited work.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties Unresolved cited work

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.827228Z

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Observation 2db31074-367d-4773-b573-08bee563cf09 · outbound

This paper cites E.; Kirklin, S.; Aykol, M.; Meredig, B.; Wolverton, C.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties E.; Kirklin, S.; Aykol, M.; Meredig, B.; Wolverton, C

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.829773Z

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Observation f4133994-b731-4e11-b11b-01cd26ce26ab · outbound

This paper cites E.; Meredig, B.; Thompson, A.; Doak, J.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties E.; Meredig, B.; Thompson, A.; Doak, J

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.832509Z

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Observation 452a31f4-4a45-46c2-8428-db39ca798d88 · outbound

This paper cites an unresolved cited work.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties Unresolved cited work

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.835107Z

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Observation 9207170c-6786-4e27-aa85-093010c7ac39 · outbound

This paper cites an unresolved cited work.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties Unresolved cited work

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.838005Z

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Observation 5f1a3e8d-5e33-4c94-9c46-b35729ae4bf2 · outbound

This paper cites an unresolved cited work.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties Unresolved cited work

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.840441Z

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Unavailable: canonical work link unavailable.

Observation 8f51d4db-a6cb-4441-9ae3-b49333c9ac29 · outbound

This paper cites F.; Romero, A.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties F.; Romero, A

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.842976Z

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Observation 4ad80e1f-baba-4738-947c-0d4244a5ae8a · outbound

This paper cites A.; Ceder, G.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties A.; Ceder, G

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.845535Z

Source-reported events for the cited work

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Observation 135a251b-8a2b-4bc9-9495-ff6d290abde3 · outbound

This paper cites Freitas, L.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties Freitas, L

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.848170Z

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Observation aee1bc5b-5496-4f32-9d0d-36f2eded33ba · outbound

This paper cites S.; Armiento, R.; Alling, B.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties S.; Armiento, R.; Alling, B

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.850638Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2d52ebaf-6e6e-4c41-89e0-862ec04ee010 · outbound

This paper cites Data-driven design of high pressure hydride superconductors using DFT and deep learning.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties Data-driven design of high pressure hydride superconductors using DFT and deep learning

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:58:20.628567Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 500a4fb6-0f04-46de-aff4-2c8d458ba6cd · outbound

This paper cites Examining Generalizability of AI Models for Catalysis.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties Examining Generalizability of AI Models for Catalysis

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:58:20.620943Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5eec165a-b7f9-44d1-99d8-06bb920cbb62 · outbound

This paper cites Thermal Conductivity Predictions with Foundation Atomistic Models.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties Thermal Conductivity Predictions with Foundation Atomistic Models

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.858735Z

Source-reported events for the cited work

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Observation e42878e8-42bb-4155-b378-c21a5d77c381 · outbound

This paper cites Fine-Tuned Language Models Generate Stable Inorganic Materials as Text.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties Fine-Tuned Language Models Generate Stable Inorganic Materials as Text

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.861645Z

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Observation 739709cc-0126-4a63-a6e6-70532ab66a8c · outbound

This paper cites Accelerated Data-Driven Discovery and Screening of Two-Dimensional Magnets Using Graph Neural Networks.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties Accelerated Data-Driven Discovery and Screening of Two-Dimensional Magnets Using Graph Neural Networks

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:58:20.612933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 53c65b30-07c0-479e-a2bd-9ad7c706c9f7 · outbound

This paper cites A foundation model for atomistic materials chemistry.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties A foundation model for atomistic materials chemistry

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.868142Z

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Unavailable: canonical work link unavailable.

Observation 916081a2-1de7-4df6-8730-c22c30068ab6 · outbound

This paper cites Orb: A Fast, Scalable Neural Network Potential.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties Orb: A Fast, Scalable Neural Network Potential

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.871007Z

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Observation e1b000d5-f105-4407-a988-0c736278930d · outbound

This paper cites OMAT24 Model.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties OMAT24 Model

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:58:20.604353Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 05283c4f-1478-49e0-ad64-4df70af72f3c · outbound

This paper cites an unresolved cited work.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties Unresolved cited work

Reference 56

Resolution: unresolved; raw_fallback, observed 2026-08-11T15:58:20.596703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 83941476-e901-4777-8a0d-d88a50a4e88f · outbound

This paper cites Atomistic Line Graph Neural Network for improved materials property predictions.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties Atomistic Line Graph Neural Network for improved materials property predictions

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:58:20.589623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 09bc04b5-cfc9-47c6-a02b-709de91318ca · outbound

This paper cites F.; Choudhary, K.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties F.; Choudhary, K

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:58:20.582133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ce2232ad-c424-47e2-9ec5-25400a8693c3 · outbound

This paper cites Tight-Binding Density Functional Theory: An Approximate Kohn-Sham DFT Scheme.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties Tight-Binding Density Functional Theory: An Approximate Kohn-Sham DFT Scheme

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:58:20.574901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2888eee8-ae24-414e-837b-207678506c64 · outbound

This paper cites an unresolved cited work.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties Unresolved cited work

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.889073Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8392da2d-62de-4006-8179-0d2a7623d524 · outbound

This paper cites https://github.com/materialsvirtuallab/matgl, 2024; Accessed: 2024-10-02.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties https://github.com/materialsvirtuallab/matgl, 2024; Accessed: 2024-10-02

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:58:20.561653Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 68f00bcc-b53e-42bf-a295-38c3cf9d8a29 · outbound

This paper cites PyTorch: An Imperative Style, High-Performance Deep Learning Library.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties PyTorch: An Imperative Style, High-Performance Deep Learning Library

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.897712Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c2843db1-9668-4487-828d-7ec57e1273c1 · outbound

This paper cites an unresolved cited work.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties Unresolved cited work

Reference 64

Resolution: unresolved; raw_fallback, observed 2026-08-11T15:58:20.552451Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation bf4c216e-ba9c-405c-bcbf-55ae00731723 · outbound

This paper cites Unified graph neural network force-field for the periodic table: solid state applications.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties Unified graph neural network force-field for the periodic table: solid state applications

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.903067Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dcc1f972-7b92-4f41-aa71-d7ae2c19d9b8 · outbound

This paper cites J.; Ceder, G.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties J.; Ceder, G

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.905581Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ab493b4-4db0-4085-8696-b0cff5ddce41 · outbound

This paper cites P.; Simm, G.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties P.; Simm, G

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.908261Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5c48d790-3996-41ae-90f2-c0b70ccc0627 · outbound

This paper cites P.; Musaelian, A.; Simm, G.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties P.; Musaelian, A.; Simm, G

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.910962Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7af69446-9253-45ee-a0e9-03d2fa6ffadd · outbound

This paper cites Atomic cluster expansion for accurate and transferable interatomic potentials.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties Atomic cluster expansion for accurate and transferable interatomic potentials

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.914133Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f764854f-eb23-4f51-8461-2e116829a92e · outbound

This paper cites Atomic cluster expansion: Completeness, efficiency and stability.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties Atomic cluster expansion: Completeness, efficiency and stability

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:58:20.526574Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f81ffc2a-8983-42b2-a7f3-7b3de2685d99 · outbound

This paper cites MACE-MP: ACE Multi-Physics Framework.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties MACE-MP: ACE Multi-Physics Framework

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:58:20.519624Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation cef41e6b-680e-4759-aee1-dbfcb2df0a2e · outbound

This paper cites Scalable Parallel Algorithm for Graph Neural Network Interatomic Potentials in Molecular Dynamics Simulations.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties Scalable Parallel Algorithm for Graph Neural Network Interatomic Potentials in Molecular Dynamics Simulations

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:58:20.512661Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e78fd01c-7749-4fac-b8e8-14a0dde0f69e · outbound

This paper cites P.; Kornbluth, M.; Molinari, N.; Smidt, T.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties P.; Kornbluth, M.; Molinari, N.; Smidt, T

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:58:20.505853Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ff937a5b-a7ae-47c2-90bd-2cc5bd60af83 · outbound

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

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.927040Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 126f4d7b-35fc-4102-b859-8872cd08c442 · outbound

This paper cites S.; Aykol, M.; Cheon, G.; Cubuk, E.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties S.; Aykol, M.; Cheon, G.; Cubuk, E

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:58:20.499110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 4c80a422-f17f-4c57-8314-c80113f1eca8 · outbound

This paper cites an unresolved cited work.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties Unresolved cited work

Reference 76

Resolution: unresolved; raw_fallback, observed 2026-08-11T15:58:20.492378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ec3d1def-cd28-47f8-84eb-f9f1adfc06da · outbound

This paper cites https://github.com/microsoft/mattersim, 2024; Accessed: 2024-12-06.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties https://github.com/microsoft/mattersim, 2024; Accessed: 2024-12-06

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:58:20.485729Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1ca73b54-e3fb-42e2-af08-9fec7fd08c59 · outbound

This paper cites https://github.com/orbital-materials/orb-models, 2024; Accessed: 2024-10-02.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties https://github.com/orbital-materials/orb-models, 2024; Accessed: 2024-10-02

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:58:20.478694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8a4d1b95-b998-4117-b023-16ac9b3ba45c · outbound

This paper cites https://www.orbitalmaterials.com/post/technical-blog-introducing-the-orb-ai-based-interatomic-potential, 2024; Accessed: 2024-10-02.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties https://www.orbitalmaterials.com/post/technical-blog-introducing-the-orb-ai-based-interatomic-potential, 2024; Accessed: 2024-10-02

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:58:20.471850Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 225ca664-d5fd-4be4-97f6-2ffb6d70f7e9 · outbound

This paper cites Matbench Discovery: A Benchmark for AI-Accelerated Materials Discovery.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties Matbench Discovery: A Benchmark for AI-Accelerated Materials Discovery

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:58:20.464105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d6bc9bb1-d63a-4f7d-93bc-cb3293605782 · outbound

This paper cites Learning to Simulate Complex Physics with Graph Networks.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties Learning to Simulate Complex Physics with Graph Networks

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.945194Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1714ff2f-16a5-494d-a07d-48b860cb2f6e · outbound

This paper cites Neural Message Passing for Quantum Chemistry.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties Neural Message Passing for Quantum Chemistry

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.948016Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f3fb8e97-0b95-441b-8c62-2f19ef0b674b · outbound

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

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.951425Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1ee95df0-f7cb-49f7-8de3-cd17e4baf720 · outbound

This paper cites EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.954193Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 86769753-8108-4350-ad1c-7749eb40d880 · outbound

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

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.957602Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ef52b396-7037-43ad-8efc-f64d66ced268 · outbound

This paper cites an unresolved cited work.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties Unresolved cited work

Reference 86

Resolution: unresolved; raw_fallback, observed 2026-08-11T15:58:20.457447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3ba8314e-5db6-4fc8-b5b3-9aa9b2ef6d88 · outbound

This paper cites https://pages.nist.gov/jarvis_leaderboard/, 2024; Accessed: 2024-10-02.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties https://pages.nist.gov/jarvis_leaderboard/, 2024; Accessed: 2024-10-02

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:58:20.449724Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation cd26bfcf-bac1-4843-8dbb-7f56568ee91a · outbound

This paper cites Systematic assessment of various universal machine-learning interatomic potentials.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties Systematic assessment of various universal machine-learning interatomic potentials

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:58:20.442268Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ec30d20f-58a4-48bb-9439-39f25734e2e4 · outbound

This paper cites Accelerating CALPHAD-based Phase Diagram Predictions in Complex Alloys Using Universal Machine Learning Potentials: Opportunities and Challenges.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties Accelerating CALPHAD-based Phase Diagram Predictions in Complex Alloys Using Universal Machine Learning Potentials: Opportunities and Challenges

Reference 89

Resolution: verified exact; local_arxiv, observed 2026-08-11T15:58:20.131552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7708791a-90e1-49c4-b899-03b5a10832a5 · outbound

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

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties Universal Machine Learning Interatomic Potentials are Ready for Phonons

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.972609Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f59de260-13f0-4b0c-a1fd-5c11d144d97a · outbound

This paper cites High-throughput Identification and Characterization of Two-dimensional Materials using Density functional theory.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties High-throughput Identification and Characterization of Two-dimensional Materials using Density functional theory

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:58:20.434874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7183bd31-c3e8-4e99-b864-0f79025ba9be · outbound

This paper cites P.; Schmidt, K.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties P.; Schmidt, K

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:58:20.427662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 4054e862-a836-4103-95db-f1548613a6cf · outbound

This paper cites an unresolved cited work.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties Unresolved cited work

Reference 93

Resolution: unresolved; raw_fallback, observed 2026-08-11T15:58:20.420419Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 288af2d5-0878-4c1c-8ba1-17b3800c7212 · outbound

This paper cites W.; Wood, B.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties W.; Wood, B

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:58:20.413077Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 87b13d07-9ff0-4dfd-81b6-a0dc85c72de9 · outbound

This paper cites A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu

Reference 95

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.985812Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d6eb3201-c508-406e-b740-ca730a039f5b · outbound

This paper cites P.; Ruzsinszky, A.; Csonka, G.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties P.; Ruzsinszky, A.; Csonka, G

Reference 96

Resolution: unresolved; no resolver link, observed 2026-08-11T15:58:19.988249Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 14ea031e-edc6-4fe9-aed0-02f8540ce570 · outbound

This paper cites 2D Universal Force Field CPU Model (Alexandria v2).

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties 2D Universal Force Field CPU Model (Alexandria v2)

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:58:20.398316Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 034cbc63-63f4-4ea2-8bdd-4ca4ab2afc4e · outbound

This paper cites Structural Relaxation Made Simple.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties Structural Relaxation Made Simple

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:58:20.391166Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d564554d-ebc3-4d40-ae9a-7429724b6ab6 · outbound

This paper cites https://wiki.fysik.dtu.dk/ase/ase/filters.html, 2025; Accessed: March 12, 2025.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties https://wiki.fysik.dtu.dk/ase/ase/filters.html, 2025; Accessed: March 12, 2025

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:58:20.383575Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7be7bf08-1101-4cd4-ac26-0bd2dc616a95 · outbound

This paper cites an unresolved cited work.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties Unresolved cited work

Reference 100

Resolution: unresolved; raw_fallback, observed 2026-08-11T15:58:20.376439Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e0108a70-3734-4ca1-90b5-ea9ee035c8bb · outbound

This paper cites T.; Parlinski, K.; Sternik, M.

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties T.; Parlinski, K.; Sternik, M

Reference 101

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:58:20.368705Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Pith citing papers

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

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

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

Resolution: unresolved; no resolver link, observed 2026-08-09T04:31:33.295572Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6e2c33a6-9441-4130-8547-44b235e16fcc · inbound

The Evolution of Machine Learning Potentials for Molecules, Reactions and Materials cites this paper.

The Evolution of Machine Learning Potentials for Molecules, Reactions and Materials CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties

Reference 297

Resolution: unresolved; no resolver link, observed 2026-08-08T13:08:50.163021Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 28f28b02-8e5a-4bfd-82b1-2206f2b62c41 · inbound

Benchmarking Universal Interatomic Potentials on Zeolite Structures cites this paper.

Benchmarking Universal Interatomic Potentials on Zeolite Structures CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties

Reference 38

Resolution: verified exact; local_arxiv, observed 2026-08-04T22:16:57.803968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.