Paper Citation Record · LEDGER

Leveraging neural network interatomic potentials for a foundation model of chemistry

As of 9 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 1 inbound Pith citation observation for arXiv:2506.18497.

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

pith.paper-citation-record.v1
2506.18497 v1

Coverage vector

measured 52 of 52 reference resolution

Typed states for the displayed outbound observations.

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One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-13T01:41:43.141782Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-13T01:42:03.089750Z

Reference resolution

52 of 52 outbound references displayed

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External citation measurements

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Outbound references

Observation 557da626-d019-47ce-ab51-7071e6b81924 · outbound

This paper cites & Sutskever, I.

Leveraging neural network interatomic potentials for a foundation model of chemistry & Sutskever, I

Reference 1

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Observation 1f28d99e-80e7-41a4-8fca-9d52749901b9 · outbound

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Leveraging neural network interatomic potentials for a foundation model of chemistry Unresolved cited work

Reference 2

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Observation 1661fe6d-6f2e-4e65-87b2-caeaff16a563 · outbound

This paper cites & Zhang, T.

Leveraging neural network interatomic potentials for a foundation model of chemistry & Zhang, T

Reference 3

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Observation 1f80b2e1-4ad8-4182-82ca-92da40e4f79b · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Leveraging neural network interatomic potentials for a foundation model of chemistry LLaMA: Open and Efficient Foundation Language Models

Reference 4

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This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Leveraging neural network interatomic potentials for a foundation model of chemistry Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 5

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Observation f3d8af20-13bc-443f-a336-784ee723474f · outbound

This paper cites M., Schwaller, P., Ortega-Guerrero, A.

Leveraging neural network interatomic potentials for a foundation model of chemistry M., Schwaller, P., Ortega-Guerrero, A

Reference 6

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Observation 4a20633f-8e02-4800-8255-2bcd40d9fae0 · outbound

This paper cites URL https://www.nature.com/articles/s41586-023-06735-9.

Leveraging neural network interatomic potentials for a foundation model of chemistry URL https://www.nature.com/articles/s41586-023-06735-9

Reference 7

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This paper cites URL https://www.microsoft.com/en-us/research/publication/ mattersim-a-deep-learning-atomistic-model-across-elements-temperatures-and-pressures/.

Leveraging neural network interatomic potentials for a foundation model of chemistry URL https://www.microsoft.com/en-us/research/publication/ mattersim-a-deep-learning-atomistic-model-across-elements-temperatures-and-pressures/

Reference 8

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This paper cites Publisher: Nature Publishing Group UK London.

Leveraging neural network interatomic potentials for a foundation model of chemistry Publisher: Nature Publishing Group UK London

Reference 9

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Observation 031ea1c5-b600-48ae-a78d-a8f49a1eae24 · outbound

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

Leveraging neural network interatomic potentials for a foundation model of chemistry Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models

Reference 10

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Observation 34b2f412-df8f-4813-a981-1c74d8a966ca · outbound

This paper cites P., Simm, G., Ortner, C.

Leveraging neural network interatomic potentials for a foundation model of chemistry P., Simm, G., Ortner, C

Reference 11

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This paper cites M., Das, A.

Leveraging neural network interatomic potentials for a foundation model of chemistry M., Das, A

Reference 12

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Observation 9d7a81d7-90c6-4d11-994d-29f71b001c85 · outbound

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

Leveraging neural network interatomic potentials for a foundation model of chemistry Orb: A Fast, Scalable Neural Network Potential

Reference 13

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This paper cites Orb-v3: atomistic simulation at scale.

Leveraging neural network interatomic potentials for a foundation model of chemistry Orb-v3: atomistic simulation at scale

Reference 14

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This paper cites & Mizukami, W.

Leveraging neural network interatomic potentials for a foundation model of chemistry & Mizukami, W

Reference 15

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Observation e16bdb32-7afd-4e1c-a0e4-fa01c162f3fc · outbound

This paper cites & Jain, A.

Leveraging neural network interatomic potentials for a foundation model of chemistry & Jain, A

Reference 16

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Observation cd555301-0bff-46cb-b723-03c56de221c1 · outbound

This paper cites URL https://www.nature.com/articles/s41524-024-01469-2.

Leveraging neural network interatomic potentials for a foundation model of chemistry URL https://www.nature.com/articles/s41524-024-01469-2

Reference 17

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Observation 10a16251-dc20-4fca-bcc1-e25c728f04d7 · outbound

This paper cites & Friederich, P.

Leveraging neural network interatomic potentials for a foundation model of chemistry & Friederich, P

Reference 18

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Observation 1c497a70-cd35-4748-a237-4a55d15c24a7 · outbound

This paper cites C.et al.Data-driven electrolyte design for lithium metal anodes.

Leveraging neural network interatomic potentials for a foundation model of chemistry C.et al.Data-driven electrolyte design for lithium metal anodes

Reference 19

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Leveraging neural network interatomic potentials for a foundation model of chemistry & Denny, R

Reference 20

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Leveraging neural network interatomic potentials for a foundation model of chemistry F., Teixeira, A

Reference 21

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Leveraging neural network interatomic potentials for a foundation model of chemistry M., Madhukar, N

Reference 22

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This paper cites URL https://pubs.rsc.org/en/content/articlelanding/ 2018/sc/c7sc02664a.

Leveraging neural network interatomic potentials for a foundation model of chemistry URL https://pubs.rsc.org/en/content/articlelanding/ 2018/sc/c7sc02664a

Reference 23

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Leveraging neural network interatomic potentials for a foundation model of chemistry Unresolved cited work

Reference 24

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Observation 553e7d76-018d-47d2-9574-9468c3cf4fc0 · outbound

This paper cites Computational Materials Science152, 60–69 (2018).

Leveraging neural network interatomic potentials for a foundation model of chemistry Computational Materials Science152, 60–69 (2018)

Reference 25

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This paper cites & Barati Farimani, A.

Leveraging neural network interatomic potentials for a foundation model of chemistry & Barati Farimani, A

Reference 26

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This paper cites Publisher: Nature Publishing Group.

Leveraging neural network interatomic potentials for a foundation model of chemistry Publisher: Nature Publishing Group

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Leveraging neural network interatomic potentials for a foundation model of chemistry Unresolved cited work

Reference 29

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This paper cites S.et al.Engineering atomic-level complexity in high-entropy and complex concentrated alloys.Nature communications10, 2090 (2019).

Leveraging neural network interatomic potentials for a foundation model of chemistry S.et al.Engineering atomic-level complexity in high-entropy and complex concentrated alloys.Nature communications10, 2090 (2019)

Reference 30

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Reference 31

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Leveraging neural network interatomic potentials for a foundation model of chemistry Unresolved cited work

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Leveraging neural network interatomic potentials for a foundation model of chemistry J., Kim, H., Jo, J

Reference 33

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Leveraging neural network interatomic potentials for a foundation model of chemistry SMILES, a chemical language and information system

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Leveraging neural network interatomic potentials for a foundation model of chemistry & Weininger, J

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This paper cites ChemBERTa-2: Towards Chemical Foundation Models.

Leveraging neural network interatomic potentials for a foundation model of chemistry ChemBERTa-2: Towards Chemical Foundation Models

Reference 36

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This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

Leveraging neural network interatomic potentials for a foundation model of chemistry RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 37

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This paper cites Can Large Language Models Understand Molecules?.

Leveraging neural network interatomic potentials for a foundation model of chemistry Can Large Language Models Understand Molecules?

Reference 38

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Leveraging neural network interatomic potentials for a foundation model of chemistry & Barati Farimani, A

Reference 39

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Leveraging neural network interatomic potentials for a foundation model of chemistry & Deny, S

Reference 40

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Leveraging neural network interatomic potentials for a foundation model of chemistry Exploring Simple Siamese Representation Learning

Reference 41

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Leveraging neural network interatomic potentials for a foundation model of chemistry Graph Contrastive Learning for Materials

Reference 42

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Leveraging neural network interatomic potentials for a foundation model of chemistry & Parrinello, M

Reference 43

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Leveraging neural network interatomic potentials for a foundation model of chemistry P., Payne, M

Reference 44

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This paper cites G., Hoogeboom, E.

Leveraging neural network interatomic potentials for a foundation model of chemistry G., Hoogeboom, E

Reference 45

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Observation 0d62f71b-2fc9-485f-8819-ec360726889b · outbound

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Leveraging neural network interatomic potentials for a foundation model of chemistry Unresolved cited work

Reference 46

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This paper cites D.et al.Benchmarking machine learning models for predicting lithium ion migration.npj Computational Materials11, 1–12 (2025).

Leveraging neural network interatomic potentials for a foundation model of chemistry D.et al.Benchmarking machine learning models for predicting lithium ion migration.npj Computational Materials11, 1–12 (2025)

Reference 47

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This paper cites URL https://www.nature.com/articles/s41467-021-26921-5.

Leveraging neural network interatomic potentials for a foundation model of chemistry URL https://www.nature.com/articles/s41467-021-26921-5

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:20:23.390683Z

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Observation a971b32a-2fae-4336-a5ac-0c8c151b5567 · outbound

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Leveraging neural network interatomic potentials for a foundation model of chemistry & Ong, S

Reference 49

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This paper cites URL https://www.nature.com/articles/ s41524-025-01606-5.

Leveraging neural network interatomic potentials for a foundation model of chemistry URL https://www.nature.com/articles/ s41524-025-01606-5

Reference 50

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Observation 3cc3cf5d-225f-478c-8ff1-e8327976030f · outbound

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Leveraging neural network interatomic potentials for a foundation model of chemistry & Rignanese, G.-M

Reference 51

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Observation 8f8131b7-584a-4a1c-83cd-647f3b9885fb · outbound

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Leveraging neural network interatomic potentials for a foundation model of chemistry & Koyama, M

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:20:22.385447Z

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Pith citing papers

Observation 09b4108a-5285-4704-aa18-2c60d5d6dcaa · inbound

Fast and Accurate Prediction of Lattice Thermal Conductivity via Machine Learning Surrogates cites this paper.

Fast and Accurate Prediction of Lattice Thermal Conductivity via Machine Learning Surrogates Leveraging neural network interatomic potentials for a foundation model of chemistry

Reference 40

Resolution: verified exact; arxiv_id, observed 2026-05-13T01:42:03.092887Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.