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Paper Citation Record · LEDGER

Mechanisms and Stability of Li Dynamics in Amorphous Li-Ti-P-S-Based Mixed Ionic-Electronic Conductors: A Machine Learning Molecular Dynamics Study

As of 19 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2506.11199.

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

pith.paper-citation-record.v1
2506.11199 v2

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

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measured 34 of 34 standing notices

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measured 0 of 0 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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Reference resolution

34 of 34 outbound references displayed

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External citation measurements

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Outbound references

Observation 858667f5-1fd4-4ff4-8d29-5f67a1e7c08f · outbound

This paper cites an unresolved cited work.

Mechanisms and Stability of Li Dynamics in Amorphous Li-Ti-P-S-Based Mixed Ionic-Electronic Conductors: A Machine Learning Molecular Dynamics Study Unresolved cited work

Reference 1

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This paper cites an unresolved cited work.

Mechanisms and Stability of Li Dynamics in Amorphous Li-Ti-P-S-Based Mixed Ionic-Electronic Conductors: A Machine Learning Molecular Dynamics Study Unresolved cited work

Reference 2

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This paper cites Kamaya, K.

Mechanisms and Stability of Li Dynamics in Amorphous Li-Ti-P-S-Based Mixed Ionic-Electronic Conductors: A Machine Learning Molecular Dynamics Study Kamaya, K

Reference 3

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This paper cites Adeli, J.D.

Mechanisms and Stability of Li Dynamics in Amorphous Li-Ti-P-S-Based Mixed Ionic-Electronic Conductors: A Machine Learning Molecular Dynamics Study Adeli, J.D

Reference 4

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This paper cites an unresolved cited work.

Mechanisms and Stability of Li Dynamics in Amorphous Li-Ti-P-S-Based Mixed Ionic-Electronic Conductors: A Machine Learning Molecular Dynamics Study Unresolved cited work

Reference 5

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This paper cites Liang, Y.

Mechanisms and Stability of Li Dynamics in Amorphous Li-Ti-P-S-Based Mixed Ionic-Electronic Conductors: A Machine Learning Molecular Dynamics Study Liang, Y

Reference 6

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This paper cites an unresolved cited work.

Mechanisms and Stability of Li Dynamics in Amorphous Li-Ti-P-S-Based Mixed Ionic-Electronic Conductors: A Machine Learning Molecular Dynamics Study Unresolved cited work

Reference 7

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This paper cites an unresolved cited work.

Mechanisms and Stability of Li Dynamics in Amorphous Li-Ti-P-S-Based Mixed Ionic-Electronic Conductors: A Machine Learning Molecular Dynamics Study Unresolved cited work

Reference 8

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This paper cites Kızılaslan, R.

Mechanisms and Stability of Li Dynamics in Amorphous Li-Ti-P-S-Based Mixed Ionic-Electronic Conductors: A Machine Learning Molecular Dynamics Study Kızılaslan, R

Reference 9

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Mechanisms and Stability of Li Dynamics in Amorphous Li-Ti-P-S-Based Mixed Ionic-Electronic Conductors: A Machine Learning Molecular Dynamics Study Unresolved cited work

Reference 10

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Mechanisms and Stability of Li Dynamics in Amorphous Li-Ti-P-S-Based Mixed Ionic-Electronic Conductors: A Machine Learning Molecular Dynamics Study Unresolved cited work

Reference 11

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This paper cites Conlin, H.

Mechanisms and Stability of Li Dynamics in Amorphous Li-Ti-P-S-Based Mixed Ionic-Electronic Conductors: A Machine Learning Molecular Dynamics Study Conlin, H

Reference 12

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Mechanisms and Stability of Li Dynamics in Amorphous Li-Ti-P-S-Based Mixed Ionic-Electronic Conductors: A Machine Learning Molecular Dynamics Study Unresolved cited work

Reference 13

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This paper cites Selvaraj, V.

Mechanisms and Stability of Li Dynamics in Amorphous Li-Ti-P-S-Based Mixed Ionic-Electronic Conductors: A Machine Learning Molecular Dynamics Study Selvaraj, V

Reference 14

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Observation 4301d97d-5f11-4daf-9e29-2bf3f235a7a8 · outbound

This paper cites Chandrasekaran, P.

Mechanisms and Stability of Li Dynamics in Amorphous Li-Ti-P-S-Based Mixed Ionic-Electronic Conductors: A Machine Learning Molecular Dynamics Study Chandrasekaran, P

Reference 15

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This paper cites Chandrasekaran, M.R.

Mechanisms and Stability of Li Dynamics in Amorphous Li-Ti-P-S-Based Mixed Ionic-Electronic Conductors: A Machine Learning Molecular Dynamics Study Chandrasekaran, M.R

Reference 16

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Mechanisms and Stability of Li Dynamics in Amorphous Li-Ti-P-S-Based Mixed Ionic-Electronic Conductors: A Machine Learning Molecular Dynamics Study Unresolved cited work

Reference 17

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This paper cites Zhang, L.

Mechanisms and Stability of Li Dynamics in Amorphous Li-Ti-P-S-Based Mixed Ionic-Electronic Conductors: A Machine Learning Molecular Dynamics Study Zhang, L

Reference 18

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This paper cites Hirel, Atomsk: A tool for manipulating and converting atomic data files.

Mechanisms and Stability of Li Dynamics in Amorphous Li-Ti-P-S-Based Mixed Ionic-Electronic Conductors: A Machine Learning Molecular Dynamics Study Hirel, Atomsk: A tool for manipulating and converting atomic data files

Reference 19

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Mechanisms and Stability of Li Dynamics in Amorphous Li-Ti-P-S-Based Mixed Ionic-Electronic Conductors: A Machine Learning Molecular Dynamics Study Unresolved cited work

Reference 20

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Mechanisms and Stability of Li Dynamics in Amorphous Li-Ti-P-S-Based Mixed Ionic-Electronic Conductors: A Machine Learning Molecular Dynamics Study Kresse, J

Reference 21

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This paper cites Kresse, J.

Mechanisms and Stability of Li Dynamics in Amorphous Li-Ti-P-S-Based Mixed Ionic-Electronic Conductors: A Machine Learning Molecular Dynamics Study Kresse, J

Reference 22

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This paper cites Bl¨ ochl, Projector augmented-wave method.

Mechanisms and Stability of Li Dynamics in Amorphous Li-Ti-P-S-Based Mixed Ionic-Electronic Conductors: A Machine Learning Molecular Dynamics Study Bl¨ ochl, Projector augmented-wave method

Reference 23

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Mechanisms and Stability of Li Dynamics in Amorphous Li-Ti-P-S-Based Mixed Ionic-Electronic Conductors: A Machine Learning Molecular Dynamics Study Perdew, K

Reference 24

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Mechanisms and Stability of Li Dynamics in Amorphous Li-Ti-P-S-Based Mixed Ionic-Electronic Conductors: A Machine Learning Molecular Dynamics Study Burke, J.P

Reference 25

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This paper cites DeePMD-kit v2: A software package for Deep Potential models.

Mechanisms and Stability of Li Dynamics in Amorphous Li-Ti-P-S-Based Mixed Ionic-Electronic Conductors: A Machine Learning Molecular Dynamics Study DeePMD-kit v2: A software package for Deep Potential models

Reference 26

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This paper cites Abadi, A.

Mechanisms and Stability of Li Dynamics in Amorphous Li-Ti-P-S-Based Mixed Ionic-Electronic Conductors: A Machine Learning Molecular Dynamics Study Abadi, A

Reference 27

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This paper cites Zhang, J.

Mechanisms and Stability of Li Dynamics in Amorphous Li-Ti-P-S-Based Mixed Ionic-Electronic Conductors: A Machine Learning Molecular Dynamics Study Zhang, J

Reference 28

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Mechanisms and Stability of Li Dynamics in Amorphous Li-Ti-P-S-Based Mixed Ionic-Electronic Conductors: A Machine Learning Molecular Dynamics Study Thompson, H.M

Reference 29

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Mechanisms and Stability of Li Dynamics in Amorphous Li-Ti-P-S-Based Mixed Ionic-Electronic Conductors: A Machine Learning Molecular Dynamics Study Unresolved cited work

Reference 31

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Mechanisms and Stability of Li Dynamics in Amorphous Li-Ti-P-S-Based Mixed Ionic-Electronic Conductors: A Machine Learning Molecular Dynamics Study Unresolved cited work

Reference 32

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Mechanisms and Stability of Li Dynamics in Amorphous Li-Ti-P-S-Based Mixed Ionic-Electronic Conductors: A Machine Learning Molecular Dynamics Study Momma, F

Reference 33

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Unavailable: canonical work link unavailable.

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This paper cites Stukowski, Visualization and analysis of atomistic simulation data with ovito–the open visu- alization tool.

Mechanisms and Stability of Li Dynamics in Amorphous Li-Ti-P-S-Based Mixed Ionic-Electronic Conductors: A Machine Learning Molecular Dynamics Study Stukowski, Visualization and analysis of atomistic simulation data with ovito–the open visu- alization tool

Reference 34

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Mechanisms and Stability of Li Dynamics in Amorphous Li-Ti-P-S-Based Mixed Ionic-Electronic Conductors: A Machine Learning Molecular Dynamics Study EV ANS, G.P

Reference 35

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Pith citing papers

No inbound Pith citation observations are available.