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Paper Citation Record · LEDGER

A Robust Machine Learned Interatomic Potential for Nb: Collision Cascade Simulations with accurate Defect Configurations

As of 14 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2502.03126.

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

pith.paper-citation-record.v1
2502.03126 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

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measured 41 of 41 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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Reference resolution

41 of 41 outbound references displayed

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External citation measurements

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Outbound references

Observation 427212a6-92b0-4b4e-abc5-b5a6fe1a7443 · outbound

This paper cites Processing, properties and applications of high-temperature niobium alloys.

A Robust Machine Learned Interatomic Potential for Nb: Collision Cascade Simulations with accurate Defect Configurations Processing, properties and applications of high-temperature niobium alloys

Reference 1

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Observation 0825c343-199b-44c4-ad04-a2c4b031eb31 · outbound

This paper cites The physical, mechanical, and irradiation behavior of niobium and niobium-base alloys.

A Robust Machine Learned Interatomic Potential for Nb: Collision Cascade Simulations with accurate Defect Configurations The physical, mechanical, and irradiation behavior of niobium and niobium-base alloys

Reference 2

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A Robust Machine Learned Interatomic Potential for Nb: Collision Cascade Simulations with accurate Defect Configurations Unresolved cited work

Reference 3

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A Robust Machine Learned Interatomic Potential for Nb: Collision Cascade Simulations with accurate Defect Configurations Unresolved cited work

Reference 4

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Observation b630470f-c4bd-44ba-8ed1-2b4cf85bc32b · outbound

This paper cites Molecular dynamics simulation of threshold displacement energy and primary damage state in Niobium.

A Robust Machine Learned Interatomic Potential for Nb: Collision Cascade Simulations with accurate Defect Configurations Molecular dynamics simulation of threshold displacement energy and primary damage state in Niobium

Reference 5

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Observation e522fc1e-459f-47b2-b5be-c7811c3bf391 · outbound

This paper cites Second nearest-neighbor modified embedded atom method potentials for bcc transition metals.

A Robust Machine Learned Interatomic Potential for Nb: Collision Cascade Simulations with accurate Defect Configurations Second nearest-neighbor modified embedded atom method potentials for bcc transition metals

Reference 6

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Observation e95bea58-39b8-420f-b14b-1e416dc063e1 · outbound

This paper cites Experimental and molecular dynamics simulation study of structure of liquid and amorphous ni62nb38 alloy.

A Robust Machine Learned Interatomic Potential for Nb: Collision Cascade Simulations with accurate Defect Configurations Experimental and molecular dynamics simulation study of structure of liquid and amorphous ni62nb38 alloy

Reference 7

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Observation a891106e-1178-4ac8-a979-35e4197b8ddb · outbound

This paper cites Force-matched embedded-atom method potential for niobium.

A Robust Machine Learned Interatomic Potential for Nb: Collision Cascade Simulations with accurate Defect Configurations Force-matched embedded-atom method potential for niobium

Reference 8

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This paper cites Universality of point defect structure in body-centered cubic metals.

A Robust Machine Learned Interatomic Potential for Nb: Collision Cascade Simulations with accurate Defect Configurations Universality of point defect structure in body-centered cubic metals

Reference 9

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Observation d07b1451-0af6-4b2a-ba3e-7e5736ba0d23 · outbound

This paper cites Symmetry-broken self-interstitial defects in chromium, molybdenum, and tung- sten.

A Robust Machine Learned Interatomic Potential for Nb: Collision Cascade Simulations with accurate Defect Configurations Symmetry-broken self-interstitial defects in chromium, molybdenum, and tung- sten

Reference 10

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Observation 7a07c16b-8051-45ab-951a-cb2784ddd336 · outbound

This paper cites Bhardwaj, Andrea E Sand, and M.

A Robust Machine Learned Interatomic Potential for Nb: Collision Cascade Simulations with accurate Defect Configurations Bhardwaj, Andrea E Sand, and M

Reference 11

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This paper cites Warrier and U.

A Robust Machine Learned Interatomic Potential for Nb: Collision Cascade Simulations with accurate Defect Configurations Warrier and U

Reference 12

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This paper cites Stability and mobility of defect clusters and dislocation loops in metals.

A Robust Machine Learned Interatomic Potential for Nb: Collision Cascade Simulations with accurate Defect Configurations Stability and mobility of defect clusters and dislocation loops in metals

Reference 13

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Observation 8fd147f0-0de9-4509-962f-e6465210df0d · outbound

This paper cites Osetsky, D.J.

A Robust Machine Learned Interatomic Potential for Nb: Collision Cascade Simulations with accurate Defect Configurations Osetsky, D.J

Reference 14

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Observation 8c8ae487-b562-4259-814a-a0bf55fde095 · outbound

This paper cites Machine learning interatomic potentials: Keys to first-principles multiscale modeling.

A Robust Machine Learned Interatomic Potential for Nb: Collision Cascade Simulations with accurate Defect Configurations Machine learning interatomic potentials: Keys to first-principles multiscale modeling

Reference 15

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Observation 23d2ac8a-5fa5-4c3b-bcc3-c56946fffafe · outbound

This paper cites Construction of machine-learning zr interatomic potentials for identifying the formation process of c-type dislocation loops.

A Robust Machine Learned Interatomic Potential for Nb: Collision Cascade Simulations with accurate Defect Configurations Construction of machine-learning zr interatomic potentials for identifying the formation process of c-type dislocation loops

Reference 16

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This paper cites Gaussian approximation potentials for body- centered-cubic transition metals.

A Robust Machine Learned Interatomic Potential for Nb: Collision Cascade Simulations with accurate Defect Configurations Gaussian approximation potentials for body- centered-cubic transition metals

Reference 17

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This paper cites Data efficiency and extrapolation trends in neural network interatomic potentials.

A Robust Machine Learned Interatomic Potential for Nb: Collision Cascade Simulations with accurate Defect Configurations Data efficiency and extrapolation trends in neural network interatomic potentials

Reference 18

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Observation d51ddc98-9e2f-4347-ac28-eb43d3d7c287 · outbound

This paper cites Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials.

A Robust Machine Learned Interatomic Potential for Nb: Collision Cascade Simulations with accurate Defect Configurations Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials

Reference 19

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A Robust Machine Learned Interatomic Potential for Nb: Collision Cascade Simulations with accurate Defect Configurations E fficiency of ab-initio total energy calculations for metals and semicon- ductors using a plane-wave basis set

Reference 20

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This paper cites The vendi score: A diversity evaluation metric for machine learning.

A Robust Machine Learned Interatomic Potential for Nb: Collision Cascade Simulations with accurate Defect Configurations The vendi score: A diversity evaluation metric for machine learning

Reference 21

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A Robust Machine Learned Interatomic Potential for Nb: Collision Cascade Simulations with accurate Defect Configurations Optimal Design of Experiments

Reference 22

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A Robust Machine Learned Interatomic Potential for Nb: Collision Cascade Simulations with accurate Defect Configurations Unresolved cited work

Reference 23

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A Robust Machine Learned Interatomic Potential for Nb: Collision Cascade Simulations with accurate Defect Configurations Unresolved cited work

Reference 24

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This paper cites Identifying sub-cascades from the primary damage state of collision cascades.

A Robust Machine Learned Interatomic Potential for Nb: Collision Cascade Simulations with accurate Defect Configurations Identifying sub-cascades from the primary damage state of collision cascades

Reference 25

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This paper cites From ultrasoft pseudopotentials to the projector augmented-wave method.

A Robust Machine Learned Interatomic Potential for Nb: Collision Cascade Simulations with accurate Defect Configurations From ultrasoft pseudopotentials to the projector augmented-wave method

Reference 26

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A Robust Machine Learned Interatomic Potential for Nb: Collision Cascade Simulations with accurate Defect Configurations A sub-sampling algorithm preventing outliers

Reference 27

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A Robust Machine Learned Interatomic Potential for Nb: Collision Cascade Simulations with accurate Defect Configurations A Performance and Cost Assessment of Machine Learning Interatomic Potentials

Reference 28

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This paper cites Fitsnap: Atomistic machine learning with lammps.

A Robust Machine Learned Interatomic Potential for Nb: Collision Cascade Simulations with accurate Defect Configurations Fitsnap: Atomistic machine learning with lammps

Reference 29

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A Robust Machine Learned Interatomic Potential for Nb: Collision Cascade Simulations with accurate Defect Configurations Unresolved cited work

Reference 30

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Observation 3797c9bd-0eda-43bd-8cfd-aace4a8f9198 · outbound

This paper cites Polyvalent machine-learned potential for cobalt: From bulk to nanoparticles.

A Robust Machine Learned Interatomic Potential for Nb: Collision Cascade Simulations with accurate Defect Configurations Polyvalent machine-learned potential for cobalt: From bulk to nanoparticles

Reference 31

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A Robust Machine Learned Interatomic Potential for Nb: Collision Cascade Simulations with accurate Defect Configurations Optuna: A next-generation hyperparameter optimization framework

Reference 32

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A Robust Machine Learned Interatomic Potential for Nb: Collision Cascade Simulations with accurate Defect Configurations Unresolved cited work

Reference 33

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 7d2bd744-4e0a-4242-bac1-29066aa8fa7f · outbound

This paper cites Bhardwaj, Harsh Hemanu, M.

A Robust Machine Learned Interatomic Potential for Nb: Collision Cascade Simulations with accurate Defect Configurations Bhardwaj, Harsh Hemanu, M

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T05:53:19.824873Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-09T05:53:18.628531Z digest=sha256:c85e53249aae6fe9817dba31faf2324076da51ef21519e3e77e1ebff45146a99

Observation 9c55648e-0f45-4725-8097-0613095844b1 · outbound

This paper cites Bhardwaj, Andrea E.

A Robust Machine Learned Interatomic Potential for Nb: Collision Cascade Simulations with accurate Defect Configurations Bhardwaj, Andrea E

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T05:53:19.749060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-09T05:53:18.659876Z digest=sha256:b832b6057ea49fe2ff59633b689573acd261b5638f6deef900245a3dd3d1762c

Observation a2846f49-5126-4951-97f1-54925775b097 · outbound

This paper cites Self-interstitial atom defects in bcc transition metals: Group- specific trends.

A Robust Machine Learned Interatomic Potential for Nb: Collision Cascade Simulations with accurate Defect Configurations Self-interstitial atom defects in bcc transition metals: Group- specific trends

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T05:53:19.662929Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-09T05:53:18.706552Z digest=sha256:278a6ab20badf1b067ceb7054a967801d59dd3256a4c7de3aec5843e22ba3352

Observation 84487a1f-12c9-457b-b9c9-dd98aaaa01fa · outbound

This paper cites Crc handbook of chemistry and physics, 100th edn., 2019–2020, 2019.

A Robust Machine Learned Interatomic Potential for Nb: Collision Cascade Simulations with accurate Defect Configurations Crc handbook of chemistry and physics, 100th edn., 2019–2020, 2019

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T05:53:19.569551Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-09T05:53:18.764051Z digest=sha256:92cc864531d3e2e26e15f1fe38494918cf97f44aa5207fa89955575c4f9ac850

Observation 52646708-c2c7-4c3f-a748-adece8fce0f1 · outbound

This paper cites Bhardwaj, Andrea E.

A Robust Machine Learned Interatomic Potential for Nb: Collision Cascade Simulations with accurate Defect Configurations Bhardwaj, Andrea E

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T05:53:19.515864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-09T05:53:18.804266Z digest=sha256:bedd8376ad2a6527411f71b192ce0cedabf6db1620b76cb9f0aa121a06b1e488

Observation a0f768dd-b67d-453b-be13-ba2b371479fd · outbound

This paper cites Molecular dynamics simulations of the defect evolution in tungsten on successive collision cascades, 2024.

A Robust Machine Learned Interatomic Potential for Nb: Collision Cascade Simulations with accurate Defect Configurations Molecular dynamics simulations of the defect evolution in tungsten on successive collision cascades, 2024

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T05:53:19.495271Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-09T05:53:18.845405Z digest=sha256:48dec3786e6f6e1ece1661faea0c444eae57a7fa0f775270d628041263002339

Observation e0dcdf40-c923-463e-973e-9704deb26da8 · outbound

This paper cites Formation mechanism of⟨111⟩ interstitial dislocation loops from irradiation-induced c15 clusters in tungsten.

A Robust Machine Learned Interatomic Potential for Nb: Collision Cascade Simulations with accurate Defect Configurations Formation mechanism of⟨111⟩ interstitial dislocation loops from irradiation-induced c15 clusters in tungsten

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T05:53:19.475242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-09T05:53:18.896219Z digest=sha256:4aa1a6d78468dedc182ae00b1a4ebad4d8432158af5d983a60c8883aaa30910f

Observation 9f460903-8669-4f61-ae30-5b78011d4fac · outbound

This paper cites D ´ezerald, M.-C.

A Robust Machine Learned Interatomic Potential for Nb: Collision Cascade Simulations with accurate Defect Configurations D ´ezerald, M.-C

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T05:53:19.450618Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-09T05:53:18.938342Z digest=sha256:809205bb09f9d9e78c902d3f120ba41e84a7129b3beb1e635b1b2d45d9045486

Pith citing papers

No inbound Pith citation observations are available.