Paper Citation Record · LEDGER

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture

As of 13 August 2026, this Paper Citation Record lists 85 of 85 outbound references and 2 inbound Pith citation observations for arXiv:2506.09256.

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

pith.paper-citation-record.v1
2506.09256 v1

Coverage vector

measured 85 of 85 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:57:44.026457Z

measured 87 of 87 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:07:38.317650Z

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

85 of 85 outbound references displayed

verified exact0
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External citation measurements

0
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 7f306c4c-8d56-420d-a3d6-f08d0c9ffbc0 · outbound

This paper cites S.; Lively, R.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture S.; Lively, R

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T04:57:36.692662Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 48d6747d-9c38-45fd-aed4-bcf6d906c1fb · outbound

This paper cites The Role of Direct Air Capture in Mitigation of Anthropogenic Greenhouse Gas Emissions.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture The Role of Direct Air Capture in Mitigation of Anthropogenic Greenhouse Gas Emissions

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T04:57:36.772019Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f20b638e-3f6a-4f82-9b98-244b9c66a790 · outbound

This paper cites S.; Murdock, C.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture S.; Murdock, C

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T04:57:36.822180Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8dbc7451-3fe6-4934-b25d-dcee15c5e86f · outbound

This paper cites H.; Landa, H.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture H.; Landa, H

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T04:57:36.888604Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 86ca1210-b003-4c06-9435-7f3479de9876 · outbound

This paper cites E.; O’Keeffe, M.; Yaghi, O.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture E.; O’Keeffe, M.; Yaghi, O

Reference 5

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Observation 1ffb0af3-4b58-4b0f-97c1-c95dcd72636f · outbound

This paper cites M.; Nandy, A.; Jablonka, K.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture M.; Nandy, A.; Jablonka, K

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:44.754887Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 1cbe10a5-e7e7-4a57-bb5b-dcd91c489eb9 · outbound

This paper cites Q.; Hupp, J.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture Q.; Hupp, J

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:44.748147Z

Source-reported events for the cited work

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Observation 8ddd6ce2-74ae-4533-9313-3bf7953ef09c · outbound

This paper cites K.; Ooe, H.; Hasegawa, U.; Yamane, S.; Yamada, H.; van Duin, A.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture K.; Ooe, H.; Hasegawa, U.; Yamane, S.; Yamada, H.; van Duin, A

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:44.741249Z

Source-reported events for the cited work

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Observation b9fcef6e-3ad5-481b-9892-e512949d9074 · outbound

This paper cites K.; Tenhu, H.; Hietala, S.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture K.; Tenhu, H.; Hietala, S

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:44.734296Z

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Observation ee260738-683f-42e2-a591-235f2a632646 · outbound

This paper cites an unresolved cited work.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture Unresolved cited work

Reference 10

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Observation 429d4d5b-1197-44db-b7f4-84565492a838 · outbound

This paper cites an unresolved cited work.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture Unresolved cited work

Reference 11

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Observation 105e6d98-4be9-4096-b7e4-b9bfb2e816da · outbound

This paper cites an unresolved cited work.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture Unresolved cited work

Reference 12

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Observation f3de9731-c50e-4f94-a993-904fb68e783d · outbound

This paper cites S.; Daou, A.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture S.; Daou, A

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:44.706900Z

Source-reported events for the cited work

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Observation fe124be8-362e-47fe-b02b-0bf953ccce77 · outbound

This paper cites Role of Solvent-Host Interactions That Lead to Very Large Swelling of Hybrid Frameworks.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture Role of Solvent-Host Interactions That Lead to Very Large Swelling of Hybrid Frameworks

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:44.699832Z

Source-reported events for the cited work

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Observation 1e23ea37-a3a1-4743-968b-c2128b9ee48a · outbound

This paper cites A.; Oktawiec, J.; Taylor, M.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture A.; Oktawiec, J.; Taylor, M

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:44.692801Z

Source-reported events for the cited work

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Observation 89bb8292-6c60-47cc-b139-98081a161556 · outbound

This paper cites M.; Braun, E.; Witman, M.; Tiana, D.; Vlaisavljevich, B.; Smit, B.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture M.; Braun, E.; Witman, M.; Tiana, D.; Vlaisavljevich, B.; Smit, B

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:44.686146Z

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Observation b71476f0-bcda-40af-a5f8-f34986f131ef · outbound

This paper cites L.; Kuchta, B.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture L.; Kuchta, B

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:44.678410Z

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Observation 1335e0c6-23ee-4332-b11c-793ab37ab284 · outbound

This paper cites Recent advances, opportunities, and challenges in high-throughput computational screening of MOFs for gas separations.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture Recent advances, opportunities, and challenges in high-throughput computational screening of MOFs for gas separations

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:44.671722Z

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Observation bbb1a374-9604-461c-ab07-67e8b70f9941 · outbound

This paper cites an unresolved cited work.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture Unresolved cited work

Reference 19

Resolution: unresolved; raw_fallback, observed 2026-08-07T04:57:44.664721Z

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Observation 8327203e-cfc3-4b42-a763-97804513ca84 · outbound

This paper cites L.; Cleeton, C.; Ferreira, R.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture L.; Cleeton, C.; Ferreira, R

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:44.657676Z

Source-reported events for the cited work

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Observation 94e7a6dc-6e50-4300-9cd6-b22cee070c83 · outbound

This paper cites G.; Moosavi, S.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture G.; Moosavi, S

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:44.650812Z

Source-reported events for the cited work

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Observation 9920e68c-8a39-49bf-abcd-5310fa0916eb · outbound

This paper cites M.; Boulfelfel, S.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture M.; Boulfelfel, S

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:44.643820Z

Source-reported events for the cited work

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Observation e043d2ba-268e-4d18-b8e9-cd76beae879e · outbound

This paper cites M.; Goeminne, R.; Demuynck, R.; Guti \' e rrez-Sevillano, J.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture M.; Goeminne, R.; Demuynck, R.; Guti \' e rrez-Sevillano, J

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:44.636899Z

Source-reported events for the cited work

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Observation 55a91c95-8ea3-4ab2-ba53-693680c58207 · outbound

This paper cites G.; Haranczyk, M.; Slater, B.; Smit, B.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture G.; Haranczyk, M.; Slater, B.; Smit, B

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:44.629709Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c7d87ec9-e9f8-4f80-b13a-8477593e844b · outbound

This paper cites an unresolved cited work.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture Unresolved cited work

Reference 25

Resolution: unresolved; raw_fallback, observed 2026-08-07T04:57:44.622607Z

Source-reported events for the cited work

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Observation 6817b38c-8626-48e3-8614-2eb1c8e439db · outbound

This paper cites M.; Das, A.; Ulissi, Z.; Uyttendaele, M.; Medford, A.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture M.; Das, A.; Ulissi, Z.; Uyttendaele, M.; Medford, A

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:44.615377Z

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Observation 73ae991a-9ef0-408e-a7f9-1cdee1166845 · outbound

This paper cites Y.; Cho, E.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture Y.; Cho, E

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:44.608615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation fb19f99e-916b-4c2a-9525-5c4e6017112c · outbound

This paper cites S.; Chung, Y.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture S.; Chung, Y

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:44.601183Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 826604f3-9393-4d96-ba5c-3d1e43c7e518 · outbound

This paper cites High-throughput computational screening of hypothetical metal-organic frameworks with open copper sites for CO _ 2 /H _ 2 separation.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture High-throughput computational screening of hypothetical metal-organic frameworks with open copper sites for CO _ 2 /H _ 2 separation

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:44.594181Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 0534ca24-6155-4d26-a5de-bff7e0837232 · outbound

This paper cites In silico screening of 4764 computation-ready, experimental metal-organic frameworks for CO _ 2 separation.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture In silico screening of 4764 computation-ready, experimental metal-organic frameworks for CO _ 2 separation

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:44.587131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 83cea475-8f7c-4e2e-986a-d7fc1a6aa663 · outbound

This paper cites B.; Long, J.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture B.; Long, J

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:44.578743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 6118bfb5-03d5-471d-bc8a-6d5dd776b368 · outbound

This paper cites R.; Yu, K.; McDaniel, J.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture R.; Yu, K.; McDaniel, J

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:44.571575Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation fd50c833-7cbf-44a8-a437-2246b6b02fea · outbound

This paper cites A.; Schall, J.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture A.; Schall, J

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:44.564478Z

Source-reported events for the cited work

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Observation 03bb828b-b43b-4bb3-a359-a5a6e305d0aa · outbound

This paper cites K.; Schmid, R.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture K.; Schmid, R

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:44.557552Z

Source-reported events for the cited work

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Observation 9ca3089d-84ec-472c-a88c-733f3f532bb2 · outbound

This paper cites On flexible force fields for metal–organic frameworks: Recent developments and future prospects.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture On flexible force fields for metal–organic frameworks: Recent developments and future prospects

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:44.550696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation d01863f9-9000-4475-ad67-33d2bbf41a78 · outbound

This paper cites u tt, K. T.; Tkatchenko, A.; M \.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture u tt, K. T.; Tkatchenko, A.; M \

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:44.542940Z

Source-reported events for the cited work

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Observation cbc7abcf-9703-4952-a01b-09e9a80e6702 · outbound

This paper cites W.; Ong, S.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture W.; Ong, S

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:44.530778Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 8a7f42b1-25ea-4eaa-ab43-a6f327b029a6 · outbound

This paper cites an unresolved cited work.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture Unresolved cited work

Reference 38

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Source-reported events for the cited work

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Observation 4e45a7da-c85a-47c0-8588-48156dbaa151 · outbound

This paper cites an unresolved cited work.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture Unresolved cited work

Reference 39

Resolution: unresolved; raw_fallback, observed 2026-08-07T04:57:44.516925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 952ffd32-8dd3-41bc-bf62-0e7ace5d40fd · outbound

This paper cites J.; Riebesell, J.; Han, K.; Bartel, C.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture J.; Riebesell, J.; Han, K.; Bartel, C

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:44.509971Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation ba5771e2-8e20-4ebf-b5e3-47e78280af8c · outbound

This paper cites MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields

Reference 41

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Observation 03cd8a23-f9dd-479c-8754-8361d2e2efdc · outbound

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

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture Atomic cluster expansion for accurate and transferable interatomic potentials

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:44.502856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 5d3bd2bc-e586-437f-9fee-a18514a4fd06 · outbound

This paper cites A foundation model for atomistic materials chemistry.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture A foundation model for atomistic materials chemistry

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T04:57:40.395195Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bb460211-1d55-4e77-9927-e6393ee01ba5 · outbound

This paper cites S.; Iyer, S.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture S.; Iyer, S

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:44.495705Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 4d19999f-245c-42b8-bf37-55e53b2b046f · outbound

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

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture P.; Hautier, G.; Chen, W.; Richards, W

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:44.488754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation df099008-b630-42ef-bb61-b8af71d19b34 · outbound

This paper cites M.; Van Maaren, P.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture M.; Van Maaren, P

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:44.481000Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 3c387818-dca9-4d1f-b4a4-82858fd5f54c · outbound

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

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-07T04:57:40.656675Z

Source-reported events for the cited work

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Observation 1f8e9b9e-9147-4d92-84b1-623f6d7b4edf · outbound

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

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-07T04:57:40.710256Z

Source-reported events for the cited work

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Observation fd75c42f-8218-401f-a058-3d804f426311 · outbound

This paper cites Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-07T04:57:40.788685Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1cf15fb8-75af-4f66-a433-177363ec11eb · outbound

This paper cites Performance Assessment of Universal Machine Learning Interatomic Potentials: Challenges and Directions for Materials' Surfaces.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture Performance Assessment of Universal Machine Learning Interatomic Potentials: Challenges and Directions for Materials' Surfaces

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-07T04:57:40.849539Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8c6bba42-f011-4ddc-ba56-76dabfb2ca77 · outbound

This paper cites L.; Neumann Barros Ferreira, R.; Steiner, M.; Hamann, H.; Gu, G.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture L.; Neumann Barros Ferreira, R.; Steiner, M.; Hamann, H.; Gu, G

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:44.474115Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 035db42f-60ef-4c5d-8e0d-16e64755ce36 · outbound

This paper cites X.; Santos, C.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture X.; Santos, C

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:44.466681Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 6b77757d-f456-4eb0-a55b-cf9ccdea1643 · outbound

This paper cites Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-07T04:57:41.084312Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 89207496-aa4a-4295-88dc-acf4c4763db6 · outbound

This paper cites an unresolved cited work.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture Unresolved cited work

Reference 54

Resolution: unresolved; raw_fallback, observed 2026-08-07T04:57:44.459671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 591c083a-7459-4c61-ac36-f0069d34df16 · outbound

This paper cites an unresolved cited work.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture Unresolved cited work

Reference 55

Resolution: unresolved; raw_fallback, observed 2026-08-07T04:57:44.452578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b975c628-6439-420f-9138-fe354d17e6a3 · outbound

This paper cites M.; Sholl, D.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture M.; Sholl, D

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:44.444645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 110718ff-94b0-4a78-8ad1-43d0e4efb351 · outbound

This paper cites A.; Vankova, N.; Akter, I.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture A.; Vankova, N.; Akter, I

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:44.437842Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 556b441e-ba0c-4103-bcd2-3e0e42c87649 · outbound

This paper cites E.; Addicoat, M.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture E.; Addicoat, M

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:44.430805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation dcf3ee4d-035b-4dfd-a2f7-83a3f7287b4d · outbound

This paper cites P.; Burke, K.; Ernzerhof, M.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture P.; Burke, K.; Ernzerhof, M

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:44.423519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c673e254-2450-49bb-ab69-6183f064d5ad · outbound

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

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:44.416461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 27e155aa-0e54-495f-a5b2-679452134c88 · outbound

This paper cites Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:44.408667Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation eb30fa9c-9944-47f1-9ff8-c27692b095ac · outbound

This paper cites A.; Sholl, D.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture A.; Sholl, D

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:44.400549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 9cf3ff7d-55ff-4fcb-9086-25e748651f38 · outbound

This paper cites J.; Siepmann, J.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture J.; Siepmann, J

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:44.392929Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 2b7ab2c3-a421-4e9e-99af-7076f8b69f1b · outbound

This paper cites L.; Sunnarborg, A.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture L.; Sunnarborg, A

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:44.385322Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 89e647a2-f7ca-4727-afdf-045c5c4b3e76 · outbound

This paper cites an unresolved cited work.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture Unresolved cited work

Reference 65

Resolution: unresolved; raw_fallback, observed 2026-08-07T04:57:44.378079Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 6c40f2ef-9f8a-4f18-86e7-7eadba921bc0 · outbound

This paper cites L.; Chandrasekhar, J.; Madura, J.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture L.; Chandrasekhar, J.; Madura, J

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:44.370213Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b2121cec-4c1a-4ac0-a874-39024501f7b9 · outbound

This paper cites an unresolved cited work.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture Unresolved cited work

Reference 67

Resolution: unresolved; raw_fallback, observed 2026-08-07T04:57:44.363458Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e4c25abb-0f9c-4a56-96d2-7e143b5a5f4c · outbound

This paper cites Structural properties of water: Comparison of the SPC, SPCE, TIP4P, and TIP5P models of water.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture Structural properties of water: Comparison of the SPC, SPCE, TIP4P, and TIP5P models of water

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:44.356286Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 24da3bd9-f8b1-4189-bfb8-66705c625866 · outbound

This paper cites an unresolved cited work.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture Unresolved cited work

Reference 69

Resolution: unresolved; raw_fallback, observed 2026-08-07T04:57:44.349172Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation eb6a1c46-4cd8-4309-8bdf-75e1a79301e0 · outbound

This paper cites Sur le m\' e lange des gaz.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture Sur le m\' e lange des gaz

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:44.341917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation aa3d8a55-b917-4fd1-8f74-f9c732ecf155 · outbound

This paper cites an unresolved cited work.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture Unresolved cited work

Reference 71

Resolution: unresolved; raw_fallback, observed 2026-08-07T04:57:44.333653Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 1c7a84cc-6ffe-42c0-a633-cfdc09b3b0b8 · outbound

This paper cites L.; Neumann, R.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture L.; Neumann, R

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:44.325633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 54ccaf88-5bf3-407f-aeb0-45831c7b0cba · outbound

This paper cites P.; Aktulga, H.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture P.; Aktulga, H

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:44.318572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation ce7e2138-9767-4f06-8fa8-8ebc2b92ce6c · outbound

This paper cites Note sur la convergence de m \'e thodes de directions conjugu \'e es.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture Note sur la convergence de m \'e thodes de directions conjugu \'e es

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:44.311304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 650e2e89-4137-44fd-a2f9-3e3f6352bf15 · outbound

This paper cites an unresolved cited work.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture Unresolved cited work

Reference 75

Resolution: unresolved; raw_fallback, observed 2026-08-07T04:57:44.303353Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 762a360b-50f8-440e-9c81-6b1b24c3f57c · outbound

This paper cites G.; Camp, J.; Haranczyk, M.; Sikora, B.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture G.; Camp, J.; Haranczyk, M.; Sikora, B

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:44.296391Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation cf1da662-2853-4553-9445-c323cb5cd2b2 · outbound

This paper cites G.; Haldoupis, E.; Bucior, B.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture G.; Haldoupis, E.; Bucior, B

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:44.288739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 0da55a07-62f5-4ced-a180-c7942f731461 · outbound

This paper cites an unresolved cited work.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture Unresolved cited work

Reference 78

Resolution: unresolved; raw_fallback, observed 2026-08-07T04:57:44.281524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 72a72ac1-1690-4c91-bd49-ef618f1c63a3 · outbound

This paper cites A.; Woo, T.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture A.; Woo, T

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:44.273895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f145b952-0703-49df-8215-b41de36a42d0 · outbound

This paper cites A.; Burner, J.; Woo, T.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture A.; Burner, J.; Woo, T

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:44.265121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation da34b2a4-5e11-4e7f-bcdb-72ff7d2f3195 · outbound

This paper cites an unresolved cited work.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture Unresolved cited work

Reference 81

Resolution: unresolved; raw_fallback, observed 2026-08-07T04:57:44.257755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation cb1afb06-c8c4-49c5-bc81-ea755a5ccb39 · outbound

This paper cites MOFChecker: An algorithm for Validating and Correcting Metal-Organic Framework (MOF) Structures.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture MOFChecker: An algorithm for Validating and Correcting Metal-Organic Framework (MOF) Structures

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:44.250120Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c52507e2-f253-431f-befd-282c4bf0e525 · outbound

This paper cites E.; Kim, K.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture E.; Kim, K

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:44.241202Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 102a4f31-9d9d-4a35-ad44-bfbac7835af4 · outbound

This paper cites S.; Sholl, D.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture S.; Sholl, D

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:57:44.233152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b328fa25-79dd-4b87-bf03-86a79ae31119 · outbound

This paper cites Scikit-learn: Machine Learning in Python.

Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture Scikit-learn: Machine Learning in Python

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-07T04:57:44.026457Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 19058aef-c971-4dab-bbc1-659f1ff0fb68 · inbound

MOFSimBench: Evaluating Universal Machine Learning Interatomic Potentials In Metal--Organic Framework Molecular Modeling cites this paper.

MOFSimBench: Evaluating Universal Machine Learning Interatomic Potentials In Metal--Organic Framework Molecular Modeling Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T17:07:38.317650Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fd67386e-07ca-41b0-bab8-4a43ac528fd7 · inbound

Towards Accurate and Scalable High-throughput MOF Adsorption Screening: Merging Classical Force Fields and Universal Machine Learned Interatomic Potentials cites this paper.

Towards Accurate and Scalable High-throughput MOF Adsorption Screening: Merging Classical Force Fields and Universal Machine Learned Interatomic Potentials Comparing classical and machine learning force fields for modeling deformation of solid sorbents relevant for direct air capture

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-08-04T23:16:06.027046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.