Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-16T05:53:35.437505Z
Paper Citation Record · LEDGER
Towards Faster and More Compact Foundation Models for Molecular Property Prediction
As of 19 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 0 inbound Pith citation observations for arXiv:2504.19538.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
51 of 51 outbound references displayed
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External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 2b917d2a-4923-4c99-a0a9-c32c8e894af8 · outbound
This paper cites an unresolved cited work.
Towards Faster and More Compact Foundation Models for Molecular Property Prediction Unresolved cited work
Reference 1
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Observation afa1c520-5887-4421-92f7-be9ef55bbb97 · outbound
This paper cites an unresolved cited work.
Towards Faster and More Compact Foundation Models for Molecular Property Prediction Unresolved cited work
Reference 2
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Observation d76180f2-9e24-4f0b-9ad4-9aa46c7198d0 · outbound
This paper cites A foundation model for atomistic materials chemistry.
Towards Faster and More Compact Foundation Models for Molecular Property Prediction A foundation model for atomistic materials chemistry
Reference 3
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Observation d9abd36e-8f2b-45ce-a08e-fb8d5b9d9347 · outbound
This paper cites Generalized neural- network representation of high-dimensional potential-energy surfaces.
Towards Faster and More Compact Foundation Models for Molecular Property Prediction Generalized neural- network representation of high-dimensional potential-energy surfaces
Reference 4
- Resolution
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Observation a53cbc1c-5778-4c92-99f9-d1d8bee4f68c · outbound
This paper cites What is the state of neural network pruning? Proceedings of machine learning and systems , 2: 129–146, 2020.
Towards Faster and More Compact Foundation Models for Molecular Property Prediction What is the state of neural network pruning? Proceedings of machine learning and systems , 2: 129–146, 2020
Reference 5
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Observation 78f69ae2-3d26-4561-b3bf-d9b1d1974a81 · outbound
This paper cites Tucker- man, Klaus-Robert M ¨uller, and Kieron Burke.
Towards Faster and More Compact Foundation Models for Molecular Property Prediction Tucker- man, Klaus-Robert M ¨uller, and Kieron Burke
Reference 6
- Resolution
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Source-reported events for the cited work
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Observation a0265470-2e12-443a-83dc-a527d7b307eb · outbound
This paper cites Model compression.
Towards Faster and More Compact Foundation Models for Molecular Property Prediction Model compression
Reference 7
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Source-reported events for the cited work
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Observation a60a08af-0ba2-4a4d-bec0-ad7262b82f8a · outbound
This paper cites Open catalyst 2020 (oc20) dataset and community challenges.
Towards Faster and More Compact Foundation Models for Molecular Property Prediction Open catalyst 2020 (oc20) dataset and community challenges
Reference 8
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Source-reported events for the cited work
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Observation f3243e24-c823-4cda-add4-6645c345b1e8 · outbound
This paper cites A survey on deep neural network pruning-taxonomy, compari- son, analysis, and recommendations, 2024.
Towards Faster and More Compact Foundation Models for Molecular Property Prediction A survey on deep neural network pruning-taxonomy, compari- son, analysis, and recommendations, 2024
Reference 9
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Observation 07ec2fd1-a221-4e6f-97aa-ebd64b245e65 · outbound
This paper cites Machine learning of accurate energy-conserving molecular force fields.
Towards Faster and More Compact Foundation Models for Molecular Property Prediction Machine learning of accurate energy-conserving molecular force fields
Reference 10
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Source-reported events for the cited work
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Observation 239f038a-a739-48f2-83c5-0ba2fc06472d · outbound
This paper cites Accurate global machine learning force fields for molecules with hundreds of atoms.
Towards Faster and More Compact Foundation Models for Molecular Property Prediction Accurate global machine learning force fields for molecules with hundreds of atoms
Reference 11
- Resolution
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Source-reported events for the cited work
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Observation f9be11fe-12d7-40fc-9a43-9deaeb8f00db · outbound
This paper cites Imagenet: A large-scale hierarchical image database.
Towards Faster and More Compact Foundation Models for Molecular Property Prediction Imagenet: A large-scale hierarchical image database
Reference 12
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Source-reported events for the cited work
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Observation 3d5c2638-f120-4cba-9c07-76f3348c6527 · outbound
This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.
Towards Faster and More Compact Foundation Models for Molecular Property Prediction An image is worth 16x16 words: Transformers for image recognition at scale
Reference 13
- Resolution
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Source-reported events for the cited work
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Observation f986b682-809b-4688-9dcf-580fc0ba1a99 · outbound
This paper cites Benchmarking materials property prediction methods: the matbench test set and automatminer reference algorithm.
Towards Faster and More Compact Foundation Models for Molecular Property Prediction Benchmarking materials property prediction methods: the matbench test set and automatminer reference algorithm
Reference 14
- Resolution
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Source-reported events for the cited work
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Observation f33b98ab-6d77-4d3a-ad9d-95452f19817f · outbound
This paper cites Spice, a dataset of drug-like molecules and peptides for training machine learning potentials.
Towards Faster and More Compact Foundation Models for Molecular Property Prediction Spice, a dataset of drug-like molecules and peptides for training machine learning potentials
Reference 15
- Resolution
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Source-reported events for the cited work
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Observation 2c430c19-c189-443b-b78a-0c3c16577456 · outbound
This paper cites Accelerating molecular graph neu- ral networks via knowledge distillation.
Towards Faster and More Compact Foundation Models for Molecular Property Prediction Accelerating molecular graph neu- ral networks via knowledge distillation
Reference 16
- Resolution
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Observation e4de2328-0f38-4b25-a4c8-583324802a02 · outbound
This paper cites Gemnet: Universal directional graph neural networks for molecules.
Towards Faster and More Compact Foundation Models for Molecular Property Prediction Gemnet: Universal directional graph neural networks for molecules
Reference 17
- Resolution
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Source-reported events for the cited work
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Observation fb8453b9-19e0-418d-bcdd-d610c18fe4aa · outbound
This paper cites GemNet-OC: Developing Graph Neural Networks for Large and Diverse Molecular Simulation Datasets.
Towards Faster and More Compact Foundation Models for Molecular Property Prediction GemNet-OC: Developing Graph Neural Networks for Large and Diverse Molecular Simulation Datasets
Reference 18
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a51edb75-b8c6-40c4-a95a-24c5043a63c0 · outbound
This paper cites an unresolved cited work.
Towards Faster and More Compact Foundation Models for Molecular Property Prediction Unresolved cited work
Reference 19
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Observation fa88c056-5bbe-43e6-ba97-cbc8e3dbb896 · outbound
This paper cites Knowl- edge distillation in vision transformers: A critical review,.
Towards Faster and More Compact Foundation Models for Molecular Property Prediction Knowl- edge distillation in vision transformers: A critical review,
Reference 20
- Resolution
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Observation 0f98f128-e83e-47b4-91a3-969a73bec362 · outbound
This paper cites Deep residual learning for image recognition.
Towards Faster and More Compact Foundation Models for Molecular Property Prediction Deep residual learning for image recognition
Reference 21
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Source-reported events for the cited work
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Observation 80418874-26e3-45f3-88ed-f02aa2f62e55 · outbound
This paper cites Distilling the Knowledge in a Neural Network.
Towards Faster and More Compact Foundation Models for Molecular Property Prediction Distilling the Knowledge in a Neural Network
Reference 22
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8e7753f8-ee7b-44c5-b064-c0e9e8d81174 · outbound
This paper cites Lawrence Zit- nick, John R Kitchin, and Zachary W Ulissi.
Towards Faster and More Compact Foundation Models for Molecular Property Prediction Lawrence Zit- nick, John R Kitchin, and Zachary W Ulissi
Reference 23
- Resolution
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Observation dc057b7c-64de-42be-995d-c49e5fa4db35 · outbound
This paper cites MACE-OFF: Transferable Short Range Machine Learning Force Fields for Organic Molecules.
Towards Faster and More Compact Foundation Models for Molecular Property Prediction MACE-OFF: Transferable Short Range Machine Learning Force Fields for Organic Molecules
Reference 24
- Resolution
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Source-reported events for the cited work
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Observation 10a690c9-cba5-4f97-a007-269e7d498925 · outbound
This paper cites EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations.
Towards Faster and More Compact Foundation Models for Molecular Property Prediction EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations
Reference 25
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 87c4e89d-e1d9-4168-90af-e1bbb479fcc7 · outbound
This paper cites Comprehen- sive graph gradual pruning for sparse training in graph neural networks, 2022.
Towards Faster and More Compact Foundation Models for Molecular Property Prediction Comprehen- sive graph gradual pruning for sparse training in graph neural networks, 2022
Reference 26
- Resolution
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Source-reported events for the cited work
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Observation fda41279-a26c-4b0d-975a-e921c6ffa726 · outbound
This paper cites LLM- pruner: On the structural pruning of large language mod- els.
Towards Faster and More Compact Foundation Models for Molecular Property Prediction LLM- pruner: On the structural pruning of large language mod- els
Reference 27
- Resolution
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Source-reported events for the cited work
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Observation 91496f72-64c9-4d38-a2e6-e01f0a56b2dc · outbound
This paper cites Nørskov, Frank Abild-Pedersen, Felix Studt, and Thomas Bligaard.
Towards Faster and More Compact Foundation Models for Molecular Property Prediction Nørskov, Frank Abild-Pedersen, Felix Studt, and Thomas Bligaard
Reference 28
- Resolution
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Source-reported events for the cited work
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Observation fefafbad-d425-464b-ba3d-7c0ce428a7f0 · outbound
This paper cites Quantum chemistry structures and properties of 134 kilo molecules.
Towards Faster and More Compact Foundation Models for Molecular Property Prediction Quantum chemistry structures and properties of 134 kilo molecules
Reference 29
- Resolution
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Source-reported events for the cited work
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Observation f98fe073-5fdb-4d4d-842b-5ba86d5ca7a3 · outbound
This paper cites Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery.Matter, 4(5):1578–1597,.
Towards Faster and More Compact Foundation Models for Molecular Property Prediction Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery.Matter, 4(5):1578–1597,
Reference 30
- Resolution
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Observation ac8798c1-27a6-4f72-949e-e078c163cf67 · outbound
This paper cites Rosen, Victor Fung, Patrick Huck, Cody T.
Towards Faster and More Compact Foundation Models for Molecular Property Prediction Rosen, Victor Fung, Patrick Huck, Cody T
Reference 31
- Resolution
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Source-reported events for the cited work
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Observation 25c1462a-1ba9-476e-b756-b0f069b3309c · outbound
This paper cites Sabe, Thandokuhle Ntombela, Lindiwe A.
Towards Faster and More Compact Foundation Models for Molecular Property Prediction Sabe, Thandokuhle Ntombela, Lindiwe A
Reference 32
- Resolution
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Source-reported events for the cited work
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Observation ebc5856e-7bed-4c96-a937-9506b9fb6b46 · outbound
This paper cites Transition1x - a dataset for building generalizable reactive machine learning potentials.
Towards Faster and More Compact Foundation Models for Molecular Property Prediction Transition1x - a dataset for building generalizable reactive machine learning potentials
Reference 33
- Resolution
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Source-reported events for the cited work
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Observation 89669e7e-7bf7-4150-b701-d562476bb059 · outbound
This paper cites Transition1x-a dataset for building generalizable reactive machine learning potentials.
Towards Faster and More Compact Foundation Models for Molecular Property Prediction Transition1x-a dataset for building generalizable reactive machine learning potentials
Reference 34
- Resolution
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Source-reported events for the cited work
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Observation 0b62be7e-8b10-4143-8717-36e75b555917 · outbound
This paper cites Schnet: A continuous-filter con- volutional neural network for modeling quantum interac- tions.
Towards Faster and More Compact Foundation Models for Molecular Property Prediction Schnet: A continuous-filter con- volutional neural network for modeling quantum interac- tions
Reference 35
- Resolution
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Source-reported events for the cited work
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Observation 69e4b2f1-373c-4222-bf5c-7feaeecbcd20 · outbound
This paper cites Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Ba- tra.
Towards Faster and More Compact Foundation Models for Molecular Property Prediction Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Ba- tra
Reference 36
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a0fb3cf4-06ca-4d4b-90a9-48d4108ebd1e · outbound
This paper cites Lorenzini, Rui Ma, Qiang Zhu, Daniel W.
Towards Faster and More Compact Foundation Models for Molecular Property Prediction Lorenzini, Rui Ma, Qiang Zhu, Daniel W
Reference 37
- Resolution
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Source-reported events for the cited work
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Observation a47e1b95-8fc0-44da-a98b-0275eaee95f7 · outbound
This paper cites From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction.
Towards Faster and More Compact Foundation Models for Molecular Property Prediction From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction
Reference 38
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 308f40ff-07a2-4afb-88a1-5c8bfa6e204a · outbound
This paper cites Neural net pruning-why and how.
Towards Faster and More Compact Foundation Models for Molecular Property Prediction Neural net pruning-why and how
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T05:53:36.173807Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 0ce0a4c5-6709-4d9c-b918-80a314e3ece4 · outbound
This paper cites The ani-1ccx and ani- 1x data sets, coupled-cluster and density functional theory properties for molecules.
Towards Faster and More Compact Foundation Models for Molecular Property Prediction The ani-1ccx and ani- 1x data sets, coupled-cluster and density functional theory properties for molecules
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T05:53:36.101456Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 8fa54b7f-80aa-4bd0-92e5-7a0042a02ea4 · outbound
This paper cites Brab- son, Abhishek Das, Zachary Ulissi, Matt Uyttendaele, An- drew J.
Towards Faster and More Compact Foundation Models for Molecular Property Prediction Brab- son, Abhishek Das, Zachary Ulissi, Matt Uyttendaele, An- drew J
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T05:53:35.942135Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 7da60230-b17f-4684-a0e6-54597932b94b · outbound
This paper cites Zico Kolter.
Towards Faster and More Compact Foundation Models for Molecular Property Prediction Zico Kolter
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T05:53:35.924112Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 76d3791a-aac1-4598-8506-7107e122749c · outbound
This paper cites an unresolved cited work.
Towards Faster and More Compact Foundation Models for Molecular Property Prediction Unresolved cited work
Reference 43
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 9d955ece-d1ba-44d6-900f-a6206bf3333f · outbound
This paper cites The open catalyst 2022 (oc22) dataset and challenges for oxide electrocatalysts.
Towards Faster and More Compact Foundation Models for Molecular Property Prediction The open catalyst 2022 (oc22) dataset and challenges for oxide electrocatalysts
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T05:53:35.889813Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 45315606-b652-4c50-b4e0-70cc8097d954 · outbound
This paper cites A survey on knowledge distillation of large language models,.
Towards Faster and More Compact Foundation Models for Molecular Property Prediction A survey on knowledge distillation of large language models,
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T05:53:35.812222Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 3ccad3cc-242c-4433-8869-c8c20dd21ff8 · outbound
This paper cites Width & depth pruning for vision transformers.
Towards Faster and More Compact Foundation Models for Molecular Property Prediction Width & depth pruning for vision transformers
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T05:53:35.770860Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 589d5020-c11d-4ca0-9a0d-631db0ce1fdf · outbound
This paper cites Pre- training via denoising for molecular property prediction,.
Towards Faster and More Compact Foundation Models for Molecular Property Prediction Pre- training via denoising for molecular property prediction,
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T05:53:35.753000Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 19517bd9-fb54-48e1-9cf2-58e89bb43f2a · outbound
This paper cites Molkd: Distilling cross-modal knowledge in chemical reactions for molecular property prediction, 2023.
Towards Faster and More Compact Foundation Models for Molecular Property Prediction Molkd: Distilling cross-modal knowledge in chemical reactions for molecular property prediction, 2023
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T05:53:35.733859Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 586f791a-9c7d-4c30-97e7-35e681c6e17e · outbound
This paper cites Loraprune: Structured pruning meets low-rank parameter-efficient fine- tuning, 2024.
Towards Faster and More Compact Foundation Models for Molecular Property Prediction Loraprune: Structured pruning meets low-rank parameter-efficient fine- tuning, 2024
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T05:53:35.707787Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation e2e7ea31-7330-45df-9b15-31c3af072cd4 · outbound
This paper cites Uni-mol: A universal 3d molecular representation learning framework.
Towards Faster and More Compact Foundation Models for Molecular Property Prediction Uni-mol: A universal 3d molecular representation learning framework
Reference 50
- Resolution
- malformed identifier
- raw_fallback, observed 2026-08-16T05:53:35.632264Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 242d14ef-1e73-4470-a821-bcdd316f9686 · outbound
This paper cites an unresolved cited work.
Towards Faster and More Compact Foundation Models for Molecular Property Prediction Unresolved cited work
Reference 2006
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T05:53:34.811485Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
No inbound Pith citation observations are available.