Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data | Paper Citation Record

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This paper cites Each MPNN backbone is paired with architecture-specific hyperparameter ranges for network depth, hidden-channel width, and interaction cutoff.

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data Each MPNN backbone is paired with architecture-specific hyperparameter ranges for network depth, hidden-channel width, and interaction cutoff

Reference 1

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This paper cites ScienceAtScale@NERSC.

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data ScienceAtScale@NERSC

Reference 2

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This paper cites Taming multi-domain, -fidelity data: Towards founda- tion models for atomistic scale simulations.

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data Taming multi-domain, -fidelity data: Towards founda- tion models for atomistic scale simulations

Reference 3

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This paper cites DPA-2: a large atomic model as a multi-task learner.

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data DPA-2: a large atomic model as a multi-task learner

Reference 4

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This paper cites Leveraging multitask learning to improve the transferability of machine learned force fields.

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data Leveraging multitask learning to improve the transferability of machine learned force fields

Reference 5

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This paper cites Learning together: Towards foundation models for machine learning interatomic potentials with meta-learning.

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data Learning together: Towards foundation models for machine learning interatomic potentials with meta-learning

Reference 6

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Observation 49831c9a-3a15-456a-84c6-5bb81a41ef3a · outbound

This paper cites Multi-fidelity learning for interatomic potentials: low-level forces and high-level energies are all you need.

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data Multi-fidelity learning for interatomic potentials: low-level forces and high-level energies are all you need

Reference 7

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Observation f63988bf-5a28-48e6-9522-84e666e0f448 · outbound

This paper cites One to rule them all: A universal interatomic potential learning across quantum chemical levels.

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data One to rule them all: A universal interatomic potential learning across quantum chemical levels

Reference 8

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Observation c93656e0-5114-4c3c-be82-f71b4f45e73f · outbound

This paper cites Scalable training of trustworthy and energy- efficient predictive graph foundation models for atomistic materials modeling: a case study with HydraGNN.

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data Scalable training of trustworthy and energy- efficient predictive graph foundation models for atomistic materials modeling: a case study with HydraGNN

Reference 9

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Observation c813e99e-4332-4a53-9e6e-55d891ba7ea4 · outbound

This paper cites QM7-X, a comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules.

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data QM7-X, a comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules

Reference 10

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Observation dfea928c-beae-4a55-b2c0-ea981ed8c3d3 · outbound

This paper cites The QCML dataset, quantum chemistry reference data from 33.5M DFT and 14.7B semi-empirical calculations.

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data The QCML dataset, quantum chemistry reference data from 33.5M DFT and 14.7B semi-empirical calculations

Reference 11

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This paper cites Transition1x - a dataset for building generalizable reactive machine learning potentials.

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data Transition1x - a dataset for building generalizable reactive machine learning potentials

Reference 12

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Observation e300a86c-c396-4622-9370-e3cefaf6230d · outbound

This paper cites The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules.

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules

Reference 13

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Observation b48d5af6-a9e1-47b0-b983-f6b40aacdce9 · outbound

This paper cites Nabla2DFT: A universal quantum chemistry dataset of drug-like molecules and a benchmark for neural network potentials.

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data Nabla2DFT: A universal quantum chemistry dataset of drug-like molecules and a benchmark for neural network potentials

Reference 14

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Observation e922bf67-e5eb-4ef8-8fe5-b226f37a4334 · outbound

This paper cites Commentary: The Materials Project: A materials genome approach to accelerating materials innovation.

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data Commentary: The Materials Project: A materials genome approach to accelerating materials innovation

Reference 15

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This paper cites A dataset of 175k stable and metastable materials calculated with the PBEsol and SCAN functionals.

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data A dataset of 175k stable and metastable materials calculated with the PBEsol and SCAN functionals

Reference 16

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This paper cites Open catalyst 2020 (OC20) dataset and community challenges.

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data Open catalyst 2020 (OC20) dataset and community challenges

Reference 17

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This paper cites Open catalyst 2022 (OC22) dataset and challenges for oxidation electrocatalysts.

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data Open catalyst 2022 (OC22) dataset and challenges for oxidation electrocatalysts

Reference 18

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Observation c415fd3f-40ef-4c4d-92c5-7555567b03f9 · outbound

This paper cites The open catalyst 2025 (OC25) dataset and models for solid-liquid interfaces.

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data The open catalyst 2025 (OC25) dataset and models for solid-liquid interfaces

Reference 19

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Observation 8fc8a4ad-b3f6-49a6-a9c9-370651ed3389 · outbound

This paper cites The open DAC 2023 dataset and challenges for sorbent discovery in direct air capture.

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data The open DAC 2023 dataset and challenges for sorbent discovery in direct air capture

Reference 20

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Observation efa8e807-a187-4186-9168-c376659537a9 · outbound

This paper cites Open materials 2024 (OMat24) inorganic materials dataset and models.

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data Open materials 2024 (OMat24) inorganic materials dataset and models

Reference 21

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Observation ae195f7c-d9b3-4825-ab34-c7a0917f87b2 · outbound

This paper cites The open molecules 2025 (OMol25) dataset, evaluations, and models.

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data The open molecules 2025 (OMol25) dataset, evaluations, and models

Reference 22

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Observation c8461943-ca71-499e-8858-05215eba07bc · outbound

This paper cites The open polymers 2026 (OPoly26) dataset and evaluations.

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data The open polymers 2026 (OPoly26) dataset and evaluations

Reference 23

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Observation 5abafdcc-994d-4d07-a9c3-aa07704748ee · outbound

This paper cites Hydragnn v4.0, version v4.0.

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data Hydragnn v4.0, version v4.0

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T10:33:03.130031Z

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Observation 49522566-bc20-40a8-8f01-8df87e6cdc87 · outbound

This paper cites ADIOS 2: The adaptable input output system. a framework for high-performance data management.

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data ADIOS 2: The adaptable input output system. a framework for high-performance data management

Reference 25

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Observation 9809d9cb-8353-448c-a71c-2efe2e217e9c · outbound

This paper cites DDStore: Distributed data store for scalable training of graph neural networks on large atomistic modeling datasets.

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data DDStore: Distributed data store for scalable training of graph neural networks on large atomistic modeling datasets

Reference 26

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Observation 0605617b-77d0-4ace-8901-2ca0937b0ac2 · outbound

This paper cites Pushing the limit of molecular dynamics with ab initio accuracy to 100 million atoms with machine learning.

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data Pushing the limit of molecular dynamics with ab initio accuracy to 100 million atoms with machine learning

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T10:33:03.092360Z

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Observation 7a0501c3-7b08-4039-afae-9a8a9ae2aea2 · outbound

This paper cites DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics.

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T10:33:03.135721Z

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Observation 5342ccd0-7793-42de-a7bc-0f2e586574fa · outbound

This paper cites Gemnet: Universal directional graph neural networks for molecules.

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data Gemnet: Universal directional graph neural networks for molecules

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T10:33:03.164951Z

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Observation 3bfd55a4-9fb7-40d7-9cb6-a755c5832c36 · outbound

This paper cites Gemnet-oc: Developing graph neural networks for large and diverse molecular simulation datasets.

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data Gemnet-oc: Developing graph neural networks for large and diverse molecular simulation datasets

Reference 30

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Observation 91ceca3f-8500-46f4-850b-d9d535fcb3db · outbound

This paper cites MACE: Higher order equivariant message passing neural networks for fast and accurate force fields.

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data MACE: Higher order equivariant message passing neural networks for fast and accurate force fields

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T10:33:03.173954Z

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Observation e2b8a9b2-bd03-4690-994b-a2db5323c044 · outbound

This paper cites Learning local equivariant representations for large- scale atomistic dynamics.

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data Learning local equivariant representations for large- scale atomistic dynamics

Reference 32

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Observation db3cc84b-ecf2-4e34-83a1-0027f4f17aff · outbound

This paper cites Equiformer: Equivariant graph attention transformer for 3d atomistic graphs.

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data Equiformer: Equivariant graph attention transformer for 3d atomistic graphs

Reference 33

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Observation a4cab98d-0cb7-4297-b039-e4457e6396ba · outbound

This paper cites Equiformerv2: Improved equivariant transformer for scaling to higher-degree representations.

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data Equiformerv2: Improved equivariant transformer for scaling to higher-degree representations

Reference 34

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Observation c9340d31-6cb7-423f-b287-7296c711447f · outbound

This paper cites A universal graph deep learning interatomic potential for the periodic table.

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data A universal graph deep learning interatomic potential for the periodic table

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T10:33:03.165112Z

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Observation 5c9be8ca-ea5b-47dc-9611-e9ed4f9baddc · outbound

This paper cites CHGNet: Pretrained universal neural network potential for charge-informed atomistic modeling.

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data CHGNet: Pretrained universal neural network potential for charge-informed atomistic modeling

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T10:33:03.179383Z

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Observation 257c32f0-4b16-480c-a587-b246b364d5d4 · outbound

This paper cites A foundation model for atomistic simulations of the elements.

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data A foundation model for atomistic simulations of the elements

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T10:33:03.145878Z

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Observation 3b396584-2597-4b8f-b4a0-b10482b9ff39 · outbound

This paper cites Cross learning between electronic structure theories for unifying molecular, surface, and inorganic crystal foundation force fields.

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data Cross learning between electronic structure theories for unifying molecular, surface, and inorganic crystal foundation force fields

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T10:33:03.172273Z

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Observation 861762c5-acf4-4597-895a-32cd74b1c0ae · outbound

This paper cites UMA: A family of universal models for atoms.

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data UMA: A family of universal models for atoms

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T10:33:03.184618Z

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Observation 97af4d9f-2b07-43ae-99ef-903e26ecf411 · outbound

This paper cites Towards universal neural network potential for material discovery applicable to arbitrary combination of 45 elements.

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data Towards universal neural network potential for material discovery applicable to arbitrary combination of 45 elements

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T10:33:03.180784Z

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Observation 5f67bc44-fa02-47b5-8912-6a9ad4fc7697 · outbound

This paper cites Scaling deep learning for materials discovery.

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data Scaling deep learning for materials discovery

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T10:33:03.168777Z

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Observation bcd88237-4615-4f46-b2a3-f914a592f96f · outbound

This paper cites Matbench discovery – an evaluation framework for machine learning crystal stability prediction.

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data Matbench discovery – an evaluation framework for machine learning crystal stability prediction

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T10:33:03.129295Z

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Observation 12880938-51e1-402a-bf74-983d25651ffe · outbound

This paper cites Multi-modal foundation model for material design.

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data Multi-modal foundation model for material design

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T10:33:03.166694Z

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Observation 1c12568f-3625-4279-9371-c614125543e4 · outbound

This paper cites Towards foundational models for molecular learning on large-scale multi-task datasets.

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data Towards foundational models for molecular learning on large-scale multi-task datasets

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T10:33:03.190170Z

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Observation 3f86d226-8bdb-4cfe-b13e-2daf9b9a70ef · outbound

This paper cites From molecules to materials: Pre-training large generalizable models for atomic property prediction.

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data From molecules to materials: Pre-training large generalizable models for atomic property prediction

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T10:33:03.142208Z

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Observation 7cbf5833-0e73-47e1-8b0e-d8d8d1dbee82 · outbound

This paper cites PyTorch FSDP: Experiences on scaling fully sharded data parallel.

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data PyTorch FSDP: Experiences on scaling fully sharded data parallel

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T10:33:03.095645Z

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Observation 17f55e03-74e8-42cf-96cb-d043ff085610 · outbound

This paper cites E(n) equivariant graph neural networks.

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data E(n) equivariant graph neural networks

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T10:33:03.179081Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 82512fc5-3582-4009-bce2-3f229f6d9c33 · outbound

This paper cites SchNet: A continuous-filter convolutional neural network for modeling quantum interactions.

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data SchNet: A continuous-filter convolutional neural network for modeling quantum interactions

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T10:33:03.188440Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 0219b366-8eb3-4a40-9491-ae393d12d078 · outbound

This paper cites Directional message passing for molecular graphs.

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data Directional message passing for molecular graphs

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T10:33:03.098319Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 9d264fae-35e9-4c14-a84e-b8e9e09a55ae · outbound

This paper cites Equivariant message passing for the prediction of tensorial properties and molecular spectra.

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data Equivariant message passing for the prediction of tensorial properties and molecular spectra

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T10:33:03.111672Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1f21447c-2642-422a-83d1-2ade807cd765 · outbound

This paper cites Principal neighbourhood aggregation for graph nets.

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data Principal neighbourhood aggregation for graph nets

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T10:33:03.116980Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 171b546b-a79b-42d5-bdc3-2f12c03a4510 · outbound

This paper cites Omnistat: Multi-vendor HPC system monitoring.

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data Omnistat: Multi-vendor HPC system monitoring

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T10:33:03.082441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation adf84cc1-fc3c-4867-8264-b443110767f9 · outbound

This paper cites Mixed precision training.

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data Mixed precision training

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T10:33:03.084271Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d867952b-7889-458b-b272-0aef5960fbf1 · outbound

This paper cites Quantum chemistry structures and properties of 134 kilo molecules.

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data Quantum chemistry structures and properties of 134 kilo molecules

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T10:33:03.168519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 27abc023-6e5f-451b-8788-70579cb9f472 · outbound

This paper cites Machine learning of accurate energy-conserving molecular force fields.

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data Machine learning of accurate energy-conserving molecular force fields

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T10:33:03.101830Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8c71a5d6-8625-4e37-b04f-aa5ccf9851bc · outbound

This paper cites Wiggle150: Benchmarking density functionals and neural network potentials on highly strained conformers.

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data Wiggle150: Benchmarking density functionals and neural network potentials on highly strained conformers

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T10:33:03.177447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b32a0343-0648-44f3-92d8-904b6e70dbd4 · outbound

This paper cites MS25: A molecular simulation benchmark for machine learning interatomic potentials.

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data MS25: A molecular simulation benchmark for machine learning interatomic potentials

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T10:33:03.167138Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c17b7a90-9ff2-42cc-bccf-8a004f9536ec · outbound

This paper cites Materials design and discovery with high-throughput density functional theory: The Open Quantum Materials Database (OQMD).

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data Materials design and discovery with high-throughput density functional theory: The Open Quantum Materials Database (OQMD)

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T10:33:03.192323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 54d85f75-385b-4e6a-88a6-44f0fc7a363b · outbound

This paper cites An inorganic ABX3 perovskite materials dataset for target property prediction and classification using machine learning.

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data An inorganic ABX3 perovskite materials dataset for target property prediction and classification using machine learning

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T10:33:03.144114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a3a07c34-855b-4b55-b7df-fe282afc5143 · outbound

This paper cites Benchmarking materials property prediction methods: the matbench test set and automatminer reference algorithm.

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data Benchmarking materials property prediction methods: the matbench test set and automatminer reference algorithm

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T10:33:03.182733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Reference resolution

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