Paper Citation Record · LEDGER

chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations

As of 18 August 2026, this Paper Citation Record lists 74 of 74 outbound references and 3 inbound Pith citation observations for arXiv:2506.04055.

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

pith.paper-citation-record.v1
2506.04055 v1

Coverage vector

measured 74 of 74 reference resolution

Typed states for the displayed outbound observations.

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One-hop event checks from named stored sources.

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measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: arxiv_reference, observed 2026-05-11T07:35:56.810231Z

Reference resolution

74 of 74 outbound references displayed

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External citation measurements

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Outbound references

Observation 8010d9c5-2e2c-4504-95a8-37090706febe · outbound

This paper cites an unresolved cited work.

chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Unresolved cited work

Reference 1

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Unresolved cited work

Reference 2

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Observation 0430d7b2-a00e-408a-989a-540501a87e89 · outbound

This paper cites The Journal of chemical physics145(17) (2016).

chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations The Journal of chemical physics145(17) (2016)

Reference 3

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Unresolved cited work

Reference 4

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This paper cites org/10.1073/pnas.0500193102.

chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations org/10.1073/pnas.0500193102

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Unresolved cited work

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Unresolved cited work

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Unresolved cited work

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Unresolved cited work

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This paper cites Chemical Science 8(4), 3192–3203 (2017) https://doi.org/10.1039/C6SC05720A.

chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Chemical Science 8(4), 3192–3203 (2017) https://doi.org/10.1039/C6SC05720A

Reference 10

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This paper cites In: Precup, D., Teh, Y.W.

chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations In: Precup, D., Teh, Y.W

Reference 11

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This paper cites Physical Review B99(1), 014104 (2019) https://doi.org/10.1103/ PhysRevB.99.014104.

chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Physical Review B99(1), 014104 (2019) https://doi.org/10.1103/ PhysRevB.99.014104

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Unresolved cited work

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Orb-v3: atomistic simulation at scale

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations https://doi.org/10.48550/arXiv

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Unresolved cited work

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This paper cites Nature Machine Intelligence7(1), 56–67 (2025) 19 https://doi.org/10.1038/s42256-024-00956-x.

chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Nature Machine Intelligence7(1), 56–67 (2025) 19 https://doi.org/10.1038/s42256-024-00956-x

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This paper cites Nature Communications14(1), 579 (2023) https://doi.org/10.1038/ s41467-023-36329-y.

chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Nature Communications14(1), 579 (2023) https://doi.org/10.1038/ s41467-023-36329-y

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This paper cites Nature Communications 13(1), 1–11 (2022) https://doi.org/10.1038/s41467-022-29939-5.

chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Nature Communications 13(1), 1–11 (2022) https://doi.org/10.1038/s41467-022-29939-5

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations SchNet - a deep learning architecture for molecules and materials

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Equivariant message passing for the prediction of tensorial properties and molecular spectra

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations jcim.0c00451

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations https://doi.org/10.48550/ arXiv.2506.02023

Reference 26

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Unresolved cited work

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Computer Physics Communications271, 108171 (2022) https: //doi.org/10.1016/j.cpc.2021.108171

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Automated discovery of a robust interatomic potential for aluminum

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations https://doi

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations 1038/s41467-021-27241-4

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations 1063/5.0235189

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Learning Non-Local Molecular Interactions via Equivariant Local Representations and Charge Equilibration

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Ewald-based Long-Range Message Passing for Molecular Graphs

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Extending the RANGE of Graph Neural Networks: Relaying Attention Nodes for Global Encoding

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations https://doi

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This paper cites Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations.

chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Thermal Conductivity Predictions with Foundation Atomistic Models

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Universal Machine Learning Interatomic Potentials are Ready for Phonons

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Stability-Aware Training of Machine Learning Force Fields with Differentiable Boltzmann Estimators

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Computer Physics Communications310, 109512 (2025) https://doi.org/10.1016/j.cpc.2025.109512

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations In: 2021 IEEE/ACM Interna- tional Symposium on Code Generation and Optimization (CGO), pp

Reference 53

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations https://github.com/openxla/xla

Reference 54

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Observation a5e8366b-1d45-4eca-9adf-d3c4831304b8 · outbound

This paper cites https://openxla.org/xla/pjrt.

chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations https://openxla.org/xla/pjrt

Reference 55

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Observation 9cc973b2-2d73-4871-b02a-c4251d5c2f66 · outbound

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations https://doi.org/10.5281/zenodo.15009305

Reference 56

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Observation 515b4728-79a2-4044-850a-aaf4e29cf69d · outbound

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Reference 57

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Reference 58

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Observation 211db2f3-442b-42c9-8a02-7e7ada715667 · outbound

This paper cites Modelling and Simulation in Materials Science and Engineering27(8), 085015 (2019) https://doi.org/10.1088/ 1361-651X/ab4b36.

chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Modelling and Simulation in Materials Science and Engineering27(8), 085015 (2019) https://doi.org/10.1088/ 1361-651X/ab4b36

Reference 59

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Observation 7017022b-7ce2-4c23-a12e-2d6e03dacd2b · outbound

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations MODELLING AND SIMULATION IN MATERIALS SCIENCE AND ENGINEERING18(015012) (2010) https://doi

Reference 60

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Unavailable: canonical work link unavailable.

Observation d4ed82e7-1b2a-4882-a898-fdaef6af12c6 · outbound

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

chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis

Reference 61

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Observation 87314b21-482f-4870-b71c-b775137c3977 · outbound

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Unresolved cited work

Reference 62

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Observation 30da1699-6864-4fc3-8e49-fcf5a5e427ec · outbound

This paper cites Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules.

chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules

Reference 63

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Observation a798d904-4521-4205-a509-f72912e3b997 · outbound

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Unresolved cited work

Reference 64

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Observation 4e2136d9-9ef5-416c-966f-49bd137495b8 · outbound

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations https://doi.org/10.26434/chemrxiv-2024-bdfr0

Reference 65

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Observation b8679724-1b48-483c-84b8-62e5f84c5467 · outbound

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Unresolved cited work

Reference 66

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Observation f433996f-8138-45b4-830e-d00c86c9fc26 · outbound

This paper cites The Journal of chemical physics153(4) (2020).

chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations The Journal of chemical physics153(4) (2020)

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:55:22.815886Z

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Observation c61879ad-3e22-48bd-8894-945dc7d7d251 · outbound

This paper cites The Journal of Chemical Physics97(3), 1990–2001 (1992) https: //doi.org/10.1063/1.463137 24.

chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations The Journal of Chemical Physics97(3), 1990–2001 (1992) https: //doi.org/10.1063/1.463137 24

Reference 68

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Observation 072a3596-060a-4a3a-b49a-8c06393d50e0 · outbound

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Physical Review Letters98(14), 146401 (2007) https://doi.org/10.1103/PhysRevLett.98.146401

Reference 69

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Observation f69a6ca7-d96e-46de-aa79-d4e1e419653d · outbound

This paper cites Chem- ical Reviews121(16), 9759–9815 (2021) https://doi.org/10.1021/acs.chemrev.

chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Chem- ical Reviews121(16), 9759–9815 (2021) https://doi.org/10.1021/acs.chemrev

Reference 70

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Source-reported events for the cited work

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Observation 4cac73b5-63da-48ca-9581-b845052f1859 · outbound

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Physical Review B87(18), 184115 (2013) https://doi.org/10.1103/PhysRevB.87

Reference 71

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Observation 4f148481-333f-482e-928d-afc250787b63 · outbound

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Directional Message Passing for Molecular Graphs

Reference 72

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Unavailable: canonical work link unavailable.

Observation 86c59309-8b0e-4a5f-ac6a-41bfe5db53a0 · outbound

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Reference 73

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Observation 2da25387-6a79-4e05-94da-451c83cc2405 · outbound

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chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations Unresolved cited work

Reference 74

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Pith citing papers

Observation 636ee985-c19f-4338-9c19-2fa4073f2cb6 · inbound

Learning Non-Local Molecular Interactions via Equivariant Local Representations and Charge Equilibration cites this paper.

Learning Non-Local Molecular Interactions via Equivariant Local Representations and Charge Equilibration chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations

Reference 38

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Observation d05a2e8c-d40e-4783-80e8-43dcefe3a29b · inbound

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Facet: highly efficient E(3)-equivariant networks for interatomic potentials chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations

Reference 28

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Observation 40229997-df16-4be9-aad9-119f9cae0297 · inbound

Making Room for AI: Multi-GPU Molecular Dynamics with Deep Potentials in GROMACS cites this paper.

Making Room for AI: Multi-GPU Molecular Dynamics with Deep Potentials in GROMACS chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations

Reference 41

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