Paper Citation Record · LEDGER

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials

As of 4 August 2026, this Paper Citation Record lists 66 of 66 outbound references and 2 inbound Pith citation observations for arXiv:2604.27685.

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

pith.paper-citation-record.v1
2604.27685 v1

Coverage vector

measured 66 of 66 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-07T06:32:25.205347Z

measured 68 of 68 standing notices

One-hop event checks from named stored sources.

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measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-11T16:17:26.963678Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-02T00:16:24.592347Z

Reference resolution

66 of 66 outbound references displayed

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External citation measurements

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Outbound references

Observation 6ab64095-2aa8-4477-9808-d8dcbfedef5b · outbound

This paper cites an unresolved cited work.

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-05-27T10:03:57.082802Z

Source-reported events for the cited work

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Observation ffec3629-48ce-43ab-be9d-475594aaa5ba · outbound

This paper cites L., Takeuchi, I.

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials L., Takeuchi, I

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T10:03:57.086472Z

Source-reported events for the cited work

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Observation c9d6acd5-6cb0-4ccf-9e4d-fabff7b6d2c9 · outbound

This paper cites an unresolved cited work.

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-05-27T10:03:57.170585Z

Source-reported events for the cited work

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Observation 9e10c1af-1813-4ba4-84eb-450b16c5115a · outbound

This paper cites an unresolved cited work.

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-05-27T10:03:57.079162Z

Source-reported events for the cited work

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Observation 04bff199-3676-44e0-88dd-a5279af0eae7 · outbound

This paper cites an unresolved cited work.

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-05-27T10:03:57.202713Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a5474796-6e59-48a0-af4e-497abc7f0d8a · outbound

This paper cites High-throughput computational screening of Heusler compounds with phonon considerations for enhanced material discovery.

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials High-throughput computational screening of Heusler compounds with phonon considerations for enhanced material discovery

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-12T10:21:29.058087Z

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Observation 6e717dda-5799-40f0-a488-289fae8db676 · outbound

This paper cites D., Xia, Y.

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials D., Xia, Y

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T10:03:57.231049Z

Source-reported events for the cited work

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Observation 5b3fb7e9-c09a-451e-be9d-1e888109a826 · outbound

This paper cites & Mizukami, W.

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials & Mizukami, W

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T10:03:57.178484Z

Source-reported events for the cited work

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Observation 0d1a43eb-cb1b-422f-85c6-d1bd23340547 · outbound

This paper cites an unresolved cited work.

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials Unresolved cited work

Reference 9

Resolution: unresolved; raw_fallback, observed 2026-05-27T10:03:57.068127Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 919b187b-c157-4faf-b015-6d8cb75855ca · outbound

This paper cites an unresolved cited work.

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials Unresolved cited work

Reference 10

Resolution: unresolved; raw_fallback, observed 2026-05-27T10:03:57.100997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f13b1b23-f991-4b3e-9bf2-ab1866c6e8e0 · outbound

This paper cites P., Simm, G.

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials P., Simm, G

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T10:03:57.224426Z

Source-reported events for the cited work

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Observation f418b74a-fa3d-4b56-a1cb-07543212a044 · outbound

This paper cites & Jung, Y.

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials & Jung, Y

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T10:03:57.064718Z

Source-reported events for the cited work

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Observation 1b188313-8069-4a49-9938-b2225a08c033 · outbound

This paper cites an unresolved cited work.

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials Unresolved cited work

Reference 13

Resolution: unresolved; raw_fallback, observed 2026-05-27T10:03:57.075389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 71437c47-5562-46cf-aa94-150397cf4aba · outbound

This paper cites Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction.

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-12T10:21:29.017890Z

Source-reported events for the cited work

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Observation 4898582a-4e67-4eaf-915b-c30ca437d66c · outbound

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

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials Uma: A family of universal models for atoms

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-12T10:21:29.085177Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b0e10aec-ca8e-4219-810f-fe15f4a2777b · outbound

This paper cites S., Ghorbani, K., Hatam-Lee, S.

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials S., Ghorbani, K., Hatam-Lee, S

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T10:03:57.173844Z

Source-reported events for the cited work

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Observation 481e46f2-ba95-4b05-bfc2-5266c7c35ce8 · outbound

This paper cites & Marques, M.

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials & Marques, M

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T10:03:57.057904Z

Source-reported events for the cited work

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Observation 23dc3032-208b-47b9-9d68-1a29a159dd66 · outbound

This paper cites & Cao, B.

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials & Cao, B

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T10:03:57.220655Z

Source-reported events for the cited work

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Observation 1d759406-b888-45bc-8c30-866f752b8233 · outbound

This paper cites A foundation model for atomistic materials chemistry.

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials A foundation model for atomistic materials chemistry

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-18T10:16:16.878114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 81324aab-352e-4c50-a6a0-5acdb65e7504 · outbound

This paper cites & Cheng, Y.

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials & Cheng, Y

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T10:03:57.048300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d71aa4b7-e999-4bee-9aa2-8ced49f11250 · outbound

This paper cites Fine-tuning foundation models of materials interatomic potentials with frozen transfer learning.

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials Fine-tuning foundation models of materials interatomic potentials with frozen transfer learning

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-12T10:21:29.049689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 82c1b4c5-ab0a-4fc2-bf97-35a3cbb58ad7 · outbound

This paper cites Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications.

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-12T10:21:29.000123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation ecbb9d3d-dfd4-43ba-9f63-9a2f7e5a99c2 · outbound

This paper cites S., Trybel, F., Faber, F.

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials S., Trybel, F., Faber, F

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-12T10:21:29.044935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b38fd527-d66e-4d5f-8c79-6049df7afa9a · outbound

This paper cites Open Materials Generation with Stochastic Interpolants.

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials Open Materials Generation with Stochastic Interpolants

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-12T10:21:29.029744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 05e93faf-1b9c-475c-9d40-51d1bbcc6aab · outbound

This paper cites PhononBench:A Large-Scale Phonon-Based Benchmark for Dynamical Stability in Crystal Generation.

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials PhononBench:A Large-Scale Phonon-Based Benchmark for Dynamical Stability in Crystal Generation

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-06-12T02:08:23.457247Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 3bec49aa-119e-4bff-a8c5-7e086098870d · outbound

This paper cites & Tanaka, I.

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials & Tanaka, I

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T10:03:57.051761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 75da64e1-4d5c-4838-9f2e-8641504ad255 · outbound

This paper cites P., Simm, G., Ortner, C.

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials P., Simm, G., Ortner, C

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T10:03:57.054794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f531e9f1-e73d-462b-90ca-caabed894ac7 · outbound

This paper cites & Rabe, K.

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials & Rabe, K

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T10:03:57.071364Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b7a85054-4747-48cf-a21a-db1bb4142d58 · outbound

This paper cites an unresolved cited work.

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials Unresolved cited work

Reference 29

Resolution: unresolved; raw_fallback, observed 2026-05-27T10:03:57.061101Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f1211cdb-b9a0-43ae-8635-ab98ba52d915 · outbound

This paper cites A., Bentria, B., Dahame, T.

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials A., Bentria, B., Dahame, T

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T10:03:57.090073Z

Source-reported events for the cited work

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Observation 05d83e22-297d-4def-b5b3-87be2cdd3132 · outbound

This paper cites & Navrotsky, A.

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials & Navrotsky, A

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T10:03:57.096645Z

Source-reported events for the cited work

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Observation 9d6fdac0-3343-457c-a239-d22210fa2872 · outbound

This paper cites an unresolved cited work.

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials Unresolved cited work

Reference 32

Resolution: unresolved; raw_fallback, observed 2026-05-27T10:03:57.216944Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 0cd8ae86-0385-4259-951b-811388255191 · outbound

This paper cites an unresolved cited work.

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials Unresolved cited work

Reference 33

Resolution: unresolved; raw_fallback, observed 2026-05-27T10:03:57.195827Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 3cf8cf74-e149-473b-bb97-ced380f955ed · outbound

This paper cites The impact of spurious imaginary phonon modes on thermal properties of Metal-organic Frameworks.

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials The impact of spurious imaginary phonon modes on thermal properties of Metal-organic Frameworks

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-06-01T02:02:27.027094Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 3cfd4be9-dbb8-4b43-8708-013ff9edb9f7 · outbound

This paper cites From Symmetry to Stability: Structural and Electronic Transformation in Cs$_2$KInI$_6$.

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials From Symmetry to Stability: Structural and Electronic Transformation in Cs$_2$KInI$_6$

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-06-04T02:07:44.901196Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 5a7c6baf-5e5b-4841-bf8a-88b2f7457a46 · outbound

This paper cites & Yin, J.

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials & Yin, J

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T10:03:57.199049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 0f6f8d34-ebe4-4083-a822-3f0af9c262c7 · outbound

This paper cites J.et al.Dynamic Local Structure in Caesium Lead Iodide: Spatial Correlation and Transient Domains.Small20, 2303565 (2024).

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials J.et al.Dynamic Local Structure in Caesium Lead Iodide: Spatial Correlation and Transient Domains.Small20, 2303565 (2024)

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T10:03:57.182029Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 3eb1845d-eac3-4d37-afa5-4a4ec0a51cf8 · outbound

This paper cites an unresolved cited work.

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials Unresolved cited work

Reference 38

Resolution: unresolved; raw_fallback, observed 2026-05-27T10:03:57.185366Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 54cbfd03-0b0c-4d40-a2c9-f89b75ef009f · outbound

This paper cites & Sleight, A.

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials & Sleight, A

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T10:03:57.189188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 66a76346-b903-41e3-96a4-c9e704de6523 · outbound

This paper cites W.et al.An Exhaustive Symmetry Approach to Structure Determination: Phase Transitions in Bi 2 Sn2 O7.Journal of the American Chemical Society138, 8031–8042 (2016).

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials W.et al.An Exhaustive Symmetry Approach to Structure Determination: Phase Transitions in Bi 2 Sn2 O7.Journal of the American Chemical Society138, 8031–8042 (2016)

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T10:03:57.192652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 1ca4bb5c-3bfa-4310-b56d-e92acfd9dac6 · outbound

This paper cites Z.et al.A Comprehensive Assessment and Benchmark Study of Large Atomistic Foundation Models for Phonons.Advanced Intelligent Discoverye202500075 (2025).

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials Z.et al.A Comprehensive Assessment and Benchmark Study of Large Atomistic Foundation Models for Phonons.Advanced Intelligent Discoverye202500075 (2025)

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T10:03:57.210187Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 6a278bd9-e8a9-46be-906c-505d9061e659 · outbound

This paper cites A., Vandergheynst, P.

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials A., Vandergheynst, P

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-12T10:21:29.006577Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 13e2685b-ab28-47d0-8241-44925811d50e · outbound

This paper cites an unresolved cited work.

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials Unresolved cited work

Reference 43

Resolution: unresolved; raw_fallback, observed 2026-05-27T10:03:57.234298Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 2b4dad05-b55a-4329-90cd-9a0c3f350669 · outbound

This paper cites npj Computational Materials11, 9 (2025).

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials npj Computational Materials11, 9 (2025)

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T10:03:57.237966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 3cd7ac42-8893-46fc-9ade-dfeedbcbc1b6 · outbound

This paper cites https://github.com/ACEsuit/mace-foundations.

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials https://github.com/ACEsuit/mace-foundations

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T10:03:57.153056Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b99209ba-aa02-4683-9971-55af1d8933bb · outbound

This paper cites Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models.

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-05-16T23:42:26.544241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation e87bf8e2-0c5e-49d3-9c00-ef8489ea22a7 · outbound

This paper cites an unresolved cited work.

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials Unresolved cited work

Reference 47

Resolution: unresolved; raw_fallback, observed 2026-05-27T10:03:57.163434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 69def5ea-abde-4073-acd2-0f99fc7d4c49 · outbound

This paper cites PHON: A program to calculate phonons using the small displacement method.

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials PHON: A program to calculate phonons using the small displacement method

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T10:03:57.157246Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 830dba47-012a-47b5-935e-dfacf35970f3 · outbound

This paper cites First-principles Phonon Calculations with Phonopy and Phono3py.Journal of the Physical Society of Japan92, 012001 (2023).

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials First-principles Phonon Calculations with Phonopy and Phono3py.Journal of the Physical Society of Japan92, 012001 (2023)

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T10:03:57.167159Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 431b730e-86ef-4cbe-8c56-bc3a18e71d97 · outbound

This paper cites Crystal structure prediction using ab initio evolutionary techniques: principles and applications.

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials Crystal structure prediction using ab initio evolutionary techniques: principles and applications

Reference 50

Resolution: verified exact; arxiv_id, observed 2026-07-04T17:16:04.003377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 01f0e234-1532-4db6-b309-92cccc0d47d4 · outbound

This paper cites an unresolved cited work.

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials Unresolved cited work

Reference 51

Resolution: unresolved; raw_fallback, observed 2026-05-27T10:03:57.148564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 6c3d148c-3c8a-489a-a25b-ccac07cb2b02 · outbound

This paper cites an unresolved cited work.

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials Unresolved cited work

Reference 52

Resolution: unresolved; raw_fallback, observed 2026-05-27T10:03:57.140953Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 17388aaa-ab61-497f-9576-9d56dde523df · outbound

This paper cites S., Wei, L., Hu, M.

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials S., Wei, L., Hu, M

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T10:03:57.123277Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a9b40739-6f1f-4100-ba94-3af7690dfed6 · outbound

This paper cites an unresolved cited work.

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials Unresolved cited work

Reference 54

Resolution: unresolved; raw_fallback, observed 2026-05-27T10:03:57.126906Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 62bf10e5-b284-4467-8178-0b2c40b06047 · outbound

This paper cites & Ozgen, S.

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials & Ozgen, S

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T10:03:57.130151Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 426bc314-6871-419b-8412-e9f27bbaeb6d · outbound

This paper cites P.et al.Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis.Computational Materials Science68, 314–319 (2013).

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials P.et al.Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis.Computational Materials Science68, 314–319 (2013)

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T10:03:57.116458Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 41cb7a82-32c8-434a-bc2b-53f9edfbf4b6 · outbound

This paper cites an unresolved cited work.

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials Unresolved cited work

Reference 57

Resolution: unresolved; raw_fallback, observed 2026-05-27T10:03:57.119828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 85bb60cf-a1cd-4d7d-9e10-252050ca85ec · outbound

This paper cites S., Hart, G.

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials S., Hart, G

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T10:03:57.108029Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 9687dd42-81ac-4a07-98f6-33fcbf098579 · outbound

This paper cites arXiv preprint arXiv:2508.20875 , year=.

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials arXiv preprint arXiv:2508.20875 , year=

Reference 59

Resolution: verified exact; arxiv_id, observed 2026-05-12T10:21:29.064160Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 4680f3f9-2cdd-4ad0-9a08-bd8f218df80f · outbound

This paper cites LeMat-BulkUnique dataset (2023).

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials LeMat-BulkUnique dataset (2023)

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T10:03:57.104339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 6b1ad22e-60c1-46cf-b85d-d110424164bc · outbound

This paper cites & Furthm¨ uller, J.

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials & Furthm¨ uller, J

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T10:03:57.111821Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 49cfb74f-0eed-4ad7-9985-7140b294612a · outbound

This paper cites P., Burke, K.

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials P., Burke, K

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T10:03:57.137018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 53f42950-327d-4ad2-805d-674ad8e594f9 · outbound

This paper cites & Joubert, D.

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials & Joubert, D

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T10:03:57.144450Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 163b79ed-f62d-4932-a14d-e29fb9c1502a · outbound

This paper cites [object Object] (2024).

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials [object Object] (2024)

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T10:03:57.160379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 4a135578-d3fd-4be8-aa6c-187d53b1bfb7 · outbound

This paper cites K.et al.Accelerated data-driven materials science with the Materials Project.

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials K.et al.Accelerated data-driven materials science with the Materials Project

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T10:03:57.227796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 022b3f79-885d-4235-a522-8ad3a84916f6 · outbound

This paper cites population.

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials population

Reference 66

Resolution: malformed identifier; arxiv_id, observed 2026-05-12T10:21:29.024447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Pith citing papers

Observation 6aed28d7-dde4-4962-9cb8-8e26e537e649 · inbound

Towards Automated Discovery: A Review of Generative Models, Multimodal Learning and Closed-Loop Workflows in Inverse Materials Design cites this paper.

Towards Automated Discovery: A Review of Generative Models, Multimodal Learning and Closed-Loop Workflows in Inverse Materials Design VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials

Reference 90

Resolution: verified exact; local_arxiv, observed 2026-07-02T00:16:24.594057Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 2e04e8e6-bd5f-4a29-8edf-f094cafb9e8f · inbound

VASP Plugins: Linking the Vienna ab-initio Simulation Package with Python cites this paper.

VASP Plugins: Linking the Vienna ab-initio Simulation Package with Python VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials

Reference 55

Resolution: unresolved; no resolver link, observed 2026-07-11T16:17:26.963678Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.