Paper Citation Record · LEDGER

Multi-task parallelism for robust pre-training of graph foundation models on multi-source, multi-fidelity atomistic modeling data

As of 9 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2506.21788.

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

pith.paper-citation-record.v1
2506.21788 v1

Coverage vector

measured 31 of 31 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

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External citation measurements

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Outbound references

Observation 94ec8b4a-5a13-46df-9be2-662c195a5cdc · outbound

This paper cites npj Computational Materials10, 154 (2024).

Multi-task parallelism for robust pre-training of graph foundation models on multi-source, multi-fidelity atomistic modeling data npj Computational Materials10, 154 (2024)

Reference 1

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

Multi-task parallelism for robust pre-training of graph foundation models on multi-source, multi-fidelity atomistic modeling data Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models

Reference 2

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This paper cites In: Proceedings of the Twelfth International Conference on Learning Representations (2024), https://openreview.net/forum?id=Zc2aIcucwc.

Multi-task parallelism for robust pre-training of graph foundation models on multi-source, multi-fidelity atomistic modeling data In: Proceedings of the Twelfth International Conference on Learning Representations (2024), https://openreview.net/forum?id=Zc2aIcucwc

Reference 3

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Observation 92feeecf-942d-49e8-abc0-7450e93ea82f · outbound

This paper cites Advanced Func- tional Materials 34, 2404043 (2024).

Multi-task parallelism for robust pre-training of graph foundation models on multi-source, multi-fidelity atomistic modeling data Advanced Func- tional Materials 34, 2404043 (2024)

Reference 4

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This paper cites Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States) (11 2023).

Multi-task parallelism for robust pre-training of graph foundation models on multi-source, multi-fidelity atomistic modeling data Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States) (11 2023)

Reference 5

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This paper cites a framework for high-performance data manage- ment.

Multi-task parallelism for robust pre-training of graph foundation models on multi-source, multi-fidelity atomistic modeling data a framework for high-performance data manage- ment

Reference 6

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This paper cites Journal of Machine Learning Research13(25), 723–773 (2012), https://jmlr.csail.mit.edu/papers/v13/gretton12a.html.

Multi-task parallelism for robust pre-training of graph foundation models on multi-source, multi-fidelity atomistic modeling data Journal of Machine Learning Research13(25), 723–773 (2012), https://jmlr.csail.mit.edu/papers/v13/gretton12a.html

Reference 7

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This paper cites Scientific Data 8, 43 (2021).

Multi-task parallelism for robust pre-training of graph foundation models on multi-source, multi-fidelity atomistic modeling data Scientific Data 8, 43 (2021)

Reference 8

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This paper cites ChemRxiv (2023).

Multi-task parallelism for robust pre-training of graph foundation models on multi-source, multi-fidelity atomistic modeling data ChemRxiv (2023)

Reference 9

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Observation 63cb7403-a33e-468a-9a42-cf86bff2873e · outbound

This paper cites Lupo Pasini et al.

Multi-task parallelism for robust pre-training of graph foundation models on multi-source, multi-fidelity atomistic modeling data Lupo Pasini et al

Reference 10

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This paper cites In: Proceedings of the SC ’23 Workshops of The Inter- national Conference on High Performance Computing, Network, Storage, and Analysis.

Multi-task parallelism for robust pre-training of graph foundation models on multi-source, multi-fidelity atomistic modeling data In: Proceedings of the SC ’23 Workshops of The Inter- national Conference on High Performance Computing, Network, Storage, and Analysis

Reference 11

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This paper cites Journal of Supercomputing81, Article 618 (Mar 2025), open Access; Published: 14 March 2025.

Multi-task parallelism for robust pre-training of graph foundation models on multi-source, multi-fidelity atomistic modeling data Journal of Supercomputing81, Article 618 (Mar 2025), open Access; Published: 14 March 2025

Reference 12

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This paper cites https://doi.org/10.2172/2224153, https://www.osti.gov/biblio/2224153.

Multi-task parallelism for robust pre-training of graph foundation models on multi-source, multi-fidelity atomistic modeling data https://doi.org/10.2172/2224153, https://www.osti.gov/biblio/2224153

Reference 13

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This paper cites https://doi.org/10.11578/dc.20240131.1, https://www.osti.gov/biblio/2283293.

Multi-task parallelism for robust pre-training of graph foundation models on multi-source, multi-fidelity atomistic modeling data https://doi.org/10.11578/dc.20240131.1, https://www.osti.gov/biblio/2283293

Reference 14

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This paper cites In: Proceedings of the International Conference for High Performance Computing, Network- ing, Storage and Analysis.

Multi-task parallelism for robust pre-training of graph foundation models on multi-source, multi-fidelity atomistic modeling data In: Proceedings of the International Conference for High Performance Computing, Network- ing, Storage and Analysis

Reference 15

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This paper cites Advanced Materials35, 2210788 (mar 2023).

Multi-task parallelism for robust pre-training of graph foundation models on multi-source, multi-fidelity atomistic modeling data Advanced Materials35, 2210788 (mar 2023)

Reference 16

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This paper cites Sci- ence Advances 7(49), eabi7948 (2021).

Multi-task parallelism for robust pre-training of graph foundation models on multi-source, multi-fidelity atomistic modeling data Sci- ence Advances 7(49), eabi7948 (2021)

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Multi-task parallelism for robust pre-training of graph foundation models on multi-source, multi-fidelity atomistic modeling data Scientific Data 9, 64 (2022)

Reference 18

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This paper cites Scientific Data 9, 779 (2022).

Multi-task parallelism for robust pre-training of graph foundation models on multi-source, multi-fidelity atomistic modeling data Scientific Data 9, 779 (2022)

Reference 19

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Multi-task parallelism for robust pre-training of graph foundation models on multi-source, multi-fidelity atomistic modeling data Unresolved cited work

Reference 20

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Multi-task parallelism for robust pre-training of graph foundation models on multi-source, multi-fidelity atomistic modeling data In: The Twelfth International Conference on Learning Repre- sentations (2024), https://openreview.net/forum?id=PfPnugdxup

Reference 21

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This paper cites Nature Com- munications 10(1), 2903 (2019).

Multi-task parallelism for robust pre-training of graph foundation models on multi-source, multi-fidelity atomistic modeling data Nature Com- munications 10(1), 2903 (2019)

Reference 22

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Multi-task parallelism for robust pre-training of graph foundation models on multi-source, multi-fidelity atomistic modeling data Scientific Data 7, 134 (2020)

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Multi-task parallelism for robust pre-training of graph foundation models on multi-source, multi-fidelity atomistic modeling data In: Proceedings of the European Conference on Computer Vision (ECCV) Work- shops

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Multi-task parallelism for robust pre-training of graph foundation models on multi-source, multi-fidelity atomistic modeling data In: The Thirty- eighth Annual Conference on Neural Information Processing Systems (2024), https://openreview.net/forum?id=klqhrq7fvB

Reference 25

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This paper cites In: Proceedings of the AI for Accelerated Materials Design Workshop at NeurIPS 2023 (2023), https://openreview.net/forum?id=EiT2bLsfM9.

Multi-task parallelism for robust pre-training of graph foundation models on multi-source, multi-fidelity atomistic modeling data In: Proceedings of the AI for Accelerated Materials Design Workshop at NeurIPS 2023 (2023), https://openreview.net/forum?id=EiT2bLsfM9

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Multi-task parallelism for robust pre-training of graph foundation models on multi-source, multi-fidelity atomistic modeling data https://doi.org/10.1088/2053-1583/accc43

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Multi-task parallelism for robust pre-training of graph foundation models on multi-source, multi-fidelity atomistic modeling data In: Proceedings of the Sixteenth European Conference on Com- puter Systems

Reference 28

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Multi-task parallelism for robust pre-training of graph foundation models on multi-source, multi-fidelity atomistic modeling data In: Pro- ceedings of the 25th ACM SIGKDD International Conference on Knowledge Dis- covery & Data Mining

Reference 29

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Multi-task parallelism for robust pre-training of graph foundation models on multi-source, multi-fidelity atomistic modeling data DPA-2: a large atomic model as a multi-task learner

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Multi-task parallelism for robust pre-training of graph foundation models on multi-source, multi-fidelity atomistic modeling data In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discov- ery and Data Mining

Reference 31

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Pith citing papers

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