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Paper Citation Record · LEDGER

Trillion-atom molecular dynamics simulations with ab initio accuracy

As of 4 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2604.24816.

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pith.paper-citation-record.v1
2604.24816 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-08T02:58:43.988785Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

33 of 33 outbound references displayed

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External citation measurements

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Outbound references

Observation 17d6e24e-f814-441d-aad3-5f803ee46b9c · outbound

This paper cites and Bacon, D.J.

Trillion-atom molecular dynamics simulations with ab initio accuracy and Bacon, D.J

Reference 1

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This paper cites and Kang, K.

Trillion-atom molecular dynamics simulations with ab initio accuracy and Kang, K

Reference 2

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This paper cites and Huang, W.

Trillion-atom molecular dynamics simulations with ab initio accuracy and Huang, W

Reference 3

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This paper cites Behler ,\ title title Four generations of high-dimensional neural network potentials , \ https://doi.org/10.1021/acs.chemrev.0c00868 journal journal Chem.

Trillion-atom molecular dynamics simulations with ab initio accuracy Behler ,\ title title Four generations of high-dimensional neural network potentials , \ https://doi.org/10.1021/acs.chemrev.0c00868 journal journal Chem

Reference 4

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This paper cites Chemical Reviews 121(16), 10142–10186 (2021) https: //doi.org/10.1021/acs.chemrev.0c01111.

Trillion-atom molecular dynamics simulations with ab initio accuracy Chemical Reviews 121(16), 10142–10186 (2021) https: //doi.org/10.1021/acs.chemrev.0c01111

Reference 5

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Trillion-atom molecular dynamics simulations with ab initio accuracy and Behler, J

Reference 6

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This paper cites and Jiang, B.

Trillion-atom molecular dynamics simulations with ab initio accuracy and Jiang, B

Reference 7

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This paper cites and Ala-Nissila, T.

Trillion-atom molecular dynamics simulations with ab initio accuracy and Ala-Nissila, T

Reference 8

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Trillion-atom molecular dynamics simulations with ab initio accuracy and Wang, L

Reference 9

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This paper cites LAMMPS-a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales.

Trillion-atom molecular dynamics simulations with ab initio accuracy LAMMPS-a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales

Reference 10

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This paper cites Generalized neural-network representation of high-dimensional potential-energy surfaces.

Trillion-atom molecular dynamics simulations with ab initio accuracy Generalized neural-network representation of high-dimensional potential-energy surfaces

Reference 11

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This paper cites Bartók, Mike C.

Trillion-atom molecular dynamics simulations with ab initio accuracy Bartók, Mike C

Reference 12

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Trillion-atom molecular dynamics simulations with ab initio accuracy and Tucker, G.J

Reference 13

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

Trillion-atom molecular dynamics simulations with ab initio accuracy DeePMD-kit : A deep learning package for many-body potential energy representation and molecular dynamics

Reference 14

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Trillion-atom molecular dynamics simulations with ab initio accuracy Musaelian , author S

Reference 15

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Trillion-atom molecular dynamics simulations with ab initio accuracy and Zhang, L

Reference 16

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Trillion-atom molecular dynamics simulations with ab initio accuracy and Batzner, S

Reference 17

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Trillion-atom molecular dynamics simulations with ab initio accuracy and Oleynik, I.I

Reference 18

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This paper cites NEP89: Universal neuroevolution potential for inorganic and organic materials across 89 elements.

Trillion-atom molecular dynamics simulations with ab initio accuracy NEP89: Universal neuroevolution potential for inorganic and organic materials across 89 elements

Reference 19

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Trillion-atom molecular dynamics simulations with ab initio accuracy and Fan, Z

Reference 20

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Trillion-atom molecular dynamics simulations with ab initio accuracy and Fan, Z

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correction dated 2025-03-31. Source: crossref record 10.1063/5.0272090->10.1063/5.0259061:correction, observed 2026-07-11T03:03:57.5093+00:00. This notice travels one citation hop only.

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Trillion-atom molecular dynamics simulations with ab initio accuracy and Schmidhuber, J

Reference 22

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Trillion-atom molecular dynamics simulations with ab initio accuracy 2025 , keywords =

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Trillion-atom molecular dynamics simulations with ab initio accuracy Available: https://doi.org/10.1038/s41467-024-54554-x

Reference 24

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Trillion-atom molecular dynamics simulations with ab initio accuracy and Jia, W

Reference 25

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Trillion-atom molecular dynamics simulations with ab initio accuracy and Jia, W

Reference 26

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Trillion-atom molecular dynamics simulations with ab initio accuracy and Su, Y

Reference 27

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Trillion-atom molecular dynamics simulations with ab initio accuracy The interface is still the device

Reference 28

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Trillion-atom molecular dynamics simulations with ab initio accuracy and Wallace, R.M

Reference 29

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Trillion-atom molecular dynamics simulations with ab initio accuracy and Vanderzande, D.J.M

Reference 30

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Trillion-atom molecular dynamics simulations with ab initio accuracy Unresolved cited work

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Trillion-atom molecular dynamics simulations with ab initio accuracy and Lee, W.B

Reference 32

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Trillion-atom molecular dynamics simulations with ab initio accuracy and Tersoff, J

Reference 33

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Pith citing papers

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