Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction | Paper Citation Record

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This paper cites (ed.) Fourier Transform Infrared Spectroscopy: Fundamentals and Application in Functional Groups and Nanomaterials Characterization, pp.

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction (ed.) Fourier Transform Infrared Spectroscopy: Fundamentals and Application in Functional Groups and Nanomaterials Characterization, pp

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This paper cites (eds.) IR Spectroscopic Techniques to Study Isolated Biomolecules, pp.

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction (eds.) IR Spectroscopic Techniques to Study Isolated Biomolecules, pp

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This paper cites 19 Surface Science Reports70(4), 449–553 (2015) https://doi.org/10.1016/j.surfrep.

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction 19 Surface Science Reports70(4), 449–553 (2015) https://doi.org/10.1016/j.surfrep

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This paper cites e-Journal of Surface Science and Nanotechnology13, 301–306 (2015) https://doi.org/10.1380/ejssnt.2015.301.

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction e-Journal of Surface Science and Nanotechnology13, 301–306 (2015) https://doi.org/10.1380/ejssnt.2015.301

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This paper cites The Journal of Physical Chemistry C117(43), 22341–22350 (2013) https://doi.org/ 10.1021/jp402737n.

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction The Journal of Physical Chemistry C117(43), 22341–22350 (2013) https://doi.org/ 10.1021/jp402737n

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This paper cites Nature Communications12(1), 6118 (2021) https://doi.org/10.1038/s41467-021-26416-3.

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction Nature Communications12(1), 6118 (2021) https://doi.org/10.1038/s41467-021-26416-3

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This paper cites Advanced Functional Materials32(16), 2111193 (2022) https://doi.org/10.1002/adfm.202111193.

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction Advanced Functional Materials32(16), 2111193 (2022) https://doi.org/10.1002/adfm.202111193

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This paper cites International Journal of Quantum Chemistry115(3), 107–136 (2015) https://doi.org/10.1002/qua.24811.

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction International Journal of Quantum Chemistry115(3), 107–136 (2015) https://doi.org/10.1002/qua.24811

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This paper cites Nature Communications11(1), 1513 (2020) https://doi.org/10.1038/ s41467-020-15340-7.

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction Nature Communications11(1), 1513 (2020) https://doi.org/10.1038/ s41467-020-15340-7

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This paper cites new routes across old boundaries in computational spectroscopy.

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction new routes across old boundaries in computational spectroscopy

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This paper cites https://doi.org/10.1016/C2010-0-68479-3.

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction https://doi.org/10.1016/C2010-0-68479-3

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This paper cites The Journal of Physical Chemistry B 107(38), 10344–10358 (2003) https://doi.org/10.1021/jp034788u.

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction The Journal of Physical Chemistry B 107(38), 10344–10358 (2003) https://doi.org/10.1021/jp034788u

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This paper cites Chemical Physics387(1), 1–4 (2011) https://doi.org/10.1016/j.chemphys.2011.06.015.

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction Chemical Physics387(1), 1–4 (2011) https://doi.org/10.1016/j.chemphys.2011.06.015

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This paper cites Journal of Computational Chemistry33(27), 2186–2198 (2012) https: //doi.org/10.1002/jcc.23036.

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction Journal of Computational Chemistry33(27), 2186–2198 (2012) https: //doi.org/10.1002/jcc.23036

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This paper cites (eds.) Theoretical Meth- ods for Vibrational Spectroscopy and Collision Induced Dissociation in the Gas Phase, pp.

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction (eds.) Theoretical Meth- ods for Vibrational Spectroscopy and Collision Induced Dissociation in the Gas Phase, pp

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This paper cites npj Computational Materials10(1), 1–9 (2024) https: //doi.org/10.1038/s41524-024-01400-9.

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction npj Computational Materials10(1), 1–9 (2024) https: //doi.org/10.1038/s41524-024-01400-9

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Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction npj Computational Materials10(1), 1–12 (2024) https://doi.org/10.1038/ s41524-024-01236-3

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This paper cites Physical Review Letters104(13) (2010) https: //doi.org/10.1103/PhysRevLett.104.136403.

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction Physical Review Letters104(13) (2010) https: //doi.org/10.1103/PhysRevLett.104.136403

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Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction Chemical Science 8(4), 3192–3203 (2017) https://doi.org/10.1039/C6SC05720A

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Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction The Journal of Chemical Physics148(24), 241722 (2018) https://doi.org/10.1063/1.5019779

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Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction Chemical Reviews 121(16), 10073–10141 (2021) https://doi.org/10.1021/acs.chemrev.1c00022

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Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction Journal of Chemical Theory and Computation15(6), 3678–3693 (2019) https://doi.org/10.1021/acs.jctc.9b00181

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Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction The Journal of Chemical Physics 158(22), 224108 (2023) https://doi.org/10.1063/5.0150379

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Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction QMe14S, A Comprehensive and Efficient Spectral Dataset for Small Organic Molecules

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Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction The Journal of Chemical Physics153(3), 034702 (2020) https://doi.org/10.1063/5.0005084

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Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction Physical Review X8(4), 041048 (2018)

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Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction Nature Communications11(1), 5461 (2020) https://doi.org/10.1038/s41467-020-19168-z

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Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction Computational Materials Science156, 148–156 (2019) https://doi.org/10

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Observation c4be676f-5a72-465b-a9cd-7a4019f50aa2 · outbound

This paper cites npj Computational Materials6(1), 1–11 (2020) https: //doi.org/10.1038/s41524-020-0283-z.

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction npj Computational Materials6(1), 1–11 (2020) https: //doi.org/10.1038/s41524-020-0283-z

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-15T20:05:03.856798Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 11cb83ae-d6c0-4220-a8a4-6eb0caf37aae · outbound

This paper cites npj Computational Materials9(1), 1–14 (2023) https://doi.org/10.1038/s41524-023-01104-6.

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction npj Computational Materials9(1), 1–14 (2023) https://doi.org/10.1038/s41524-023-01104-6

Reference 49

Resolution: verified exact; doi, observed 2026-08-15T20:05:04.353154Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation cacc3379-9f07-45b8-8ef0-0dbf661b0ea7 · outbound

This paper cites Nature Computational Science3(3), 230–239 (2023) https: //doi.org/10.1038/s43588-023-00406-5.

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction Nature Computational Science3(3), 230–239 (2023) https: //doi.org/10.1038/s43588-023-00406-5

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-15T20:05:03.865835Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cb2d97fe-8ece-4c81-a0c1-b906381d66c5 · outbound

This paper cites npj Computational Materials9(1), 1– 11 (2023) https://doi.org/10.1038/s41524-023-01180-8.

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction npj Computational Materials9(1), 1– 11 (2023) https://doi.org/10.1038/s41524-023-01180-8

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-15T20:05:03.870614Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d8c95d75-99c9-4bb4-8229-a19c374626ac · outbound

This paper cites npj Computational Materials10(1), 1–18 (2024) https://doi.org/10.1038/s41524-024-01254-1.

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction npj Computational Materials10(1), 1–18 (2024) https://doi.org/10.1038/s41524-024-01254-1

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-15T20:05:03.875069Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 69081e19-9df8-465c-b3f9-c8dfe4c54ec3 · outbound

This paper cites an unresolved cited work.

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction Unresolved cited work

Reference 53

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:05:05.142364Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 468f533a-ef90-4e19-9319-a7f59978dd12 · outbound

This paper cites an unresolved cited work.

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction Unresolved cited work

Reference 54

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:05:05.126348Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 47d6ea3f-cd50-4773-8075-fb8e7cc08a1d · outbound

This paper cites GitLab (2024).

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction GitLab (2024)

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:05:05.111967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 723e4339-e4f2-4f9e-9653-7e354925ba57 · outbound

This paper cites The Journal of Chemical Physics153(10), 104105 (2020) https://doi.org/10.1063/5.0016004.

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction The Journal of Chemical Physics153(10), 104105 (2020) https://doi.org/10.1063/5.0016004

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-15T20:05:03.894321Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 57835519-9f7a-4a4d-b671-33f7afc96fd2 · outbound

This paper cites The Journal of Chemical Physics 24 148(24), 241733 (2018) https://doi.org/10.1063/1.5023802.

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction The Journal of Chemical Physics 24 148(24), 241733 (2018) https://doi.org/10.1063/1.5023802

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-15T20:05:03.899577Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 45c5f772-2ee1-443d-86e4-5beaf8fb617c · outbound

This paper cites an unresolved cited work.

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction Unresolved cited work

Reference 58

Resolution: verified exact; doi, observed 2026-08-15T20:05:04.289984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation aea8b9cb-d402-4f3f-a21b-23f3123e4d43 · outbound

This paper cites Computer Physics Communications180(11), 2175–2196 (2009) https://doi.org/ 10.1016/j.cpc.2009.06.022.

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction Computer Physics Communications180(11), 2175–2196 (2009) https://doi.org/ 10.1016/j.cpc.2009.06.022

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-15T20:05:03.909719Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 47c60175-8c37-40e2-9bbe-168a02d12fa9 · outbound

This paper cites Journal of Computational Physics228(22), 8367–8379 (2009) https://doi.org/10.1016/j.

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction Journal of Computational Physics228(22), 8367–8379 (2009) https://doi.org/10.1016/j

Reference 60

Resolution: malformed identifier; no resolver link, observed 2026-08-15T20:05:03.914341Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1743ee4-3808-4670-8f9f-3b9dbbf64db9 · outbound

This paper cites Computer Physics Communications192, 60–69 (2015) https://doi.org/10.1016/j.cpc.2015.02.021.

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction Computer Physics Communications192, 60–69 (2015) https://doi.org/10.1016/j.cpc.2015.02.021

Reference 61

Resolution: verified exact; doi, observed 2026-08-15T20:05:04.254176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8c2004e0-a1c1-4f20-84f2-e343e492bac8 · outbound

This paper cites an unresolved cited work.

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction Unresolved cited work

Reference 62

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:05:05.096716Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1ae2c3e8-4974-424c-b9cd-2ab188add493 · outbound

This paper cites Machine Learning: Science and Technology3(4), 045017 (2022) https: //doi.org/10.1088/2632-2153/aca005.

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction Machine Learning: Science and Technology3(4), 045017 (2022) https: //doi.org/10.1088/2632-2153/aca005

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-15T20:05:03.927660Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a7a965d6-e7b7-4d5a-be98-9480abb70bf2 · outbound

This paper cites Computer Physics Communications247, 106949 (2020) https://doi.org/10.1016/j.cpc.2019.106949.

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction Computer Physics Communications247, 106949 (2020) https://doi.org/10.1016/j.cpc.2019.106949

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-15T20:05:03.932306Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a8d5f294-ef91-44b8-83c8-a4aa03d9ba71 · outbound

This paper cites Journal of Chemical Theory and Computation17(2), 985–995 (2021) https://doi.org/10.1021/acs.jctc.0c01279.

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction Journal of Chemical Theory and Computation17(2), 985–995 (2021) https://doi.org/10.1021/acs.jctc.0c01279

Reference 65

Resolution: verified exact; doi, observed 2026-08-15T20:05:04.230045Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 816b8b99-23e9-40d6-8aa4-572bffd86d39 · outbound

This paper cites Infrared Spectra.

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction Infrared Spectra

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-15T20:05:03.940850Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 105d0451-bb33-402b-b18a-ac55a87593e0 · outbound

This paper cites Chemical Physics514, 44–54 (2018) https: //doi.org/10.1016/j.chemphys.2017.12.015.

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction Chemical Physics514, 44–54 (2018) https: //doi.org/10.1016/j.chemphys.2017.12.015

Reference 67

Resolution: verified exact; doi, observed 2026-08-15T20:05:04.206326Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation fa3db9ce-7d21-4114-875d-859a3847d694 · outbound

This paper cites an unresolved cited work.

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction Unresolved cited work

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-15T20:05:03.949169Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 57f1b466-e167-4b4b-a506-99ac53dfc90d · outbound

This paper cites The Journal of Chemical Physics99(6), 4597–4610 (1993) https: //doi.org/10.1063/1.466059.

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction The Journal of Chemical Physics99(6), 4597–4610 (1993) https: //doi.org/10.1063/1.466059

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-15T20:05:03.953436Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d89de966-c5c2-498a-8fc8-f07ceb67e3c4 · outbound

This paper cites Physical ReviewLetters102(7),073005(2009)https://doi.org/10.1103/PhysRevLett.102.

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction Physical ReviewLetters102(7),073005(2009)https://doi.org/10.1103/PhysRevLett.102

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-15T20:05:03.957827Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b9fa5dd2-372f-49fe-bb7a-102dfaa5b068 · outbound

This paper cites Springer Series in Operations Research and Financial Engineering.

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction Springer Series in Operations Research and Financial Engineering

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-15T20:05:03.962055Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ba9824dc-2d06-43ef-b106-6e4d6b11fd4e · outbound

This paper cites Journal of Physics: Condensed Matter29(27), 273002 (2017).

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction Journal of Physics: Condensed Matter29(27), 273002 (2017)

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:05:05.081341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4d21751d-bb49-4c4f-bb61-ff5329551197 · outbound

This paper cites Scientific Data1(1), 140022 (2014) https://doi.org/10.1038/sdata.2014.22.

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction Scientific Data1(1), 140022 (2014) https://doi.org/10.1038/sdata.2014.22

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-15T20:05:03.970221Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d28bf0dc-a90f-4cd6-babd-08429a76f840 · outbound

This paper cites Tech- nical Report MSR-TR-2000-65, Microsoft Research (May 2000).

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction Tech- nical Report MSR-TR-2000-65, Microsoft Research (May 2000)

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:05:05.066176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 46717b77-f205-4f81-bbe5-2bf1a36da337 · outbound

This paper cites The Journal of Chemical Physics81(8), 3684–3690 (1984) https://doi.org/10.1063/1.448118.

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction The Journal of Chemical Physics81(8), 3684–3690 (1984) https://doi.org/10.1063/1.448118

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-15T20:05:03.979339Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ea2a404-0b5c-41b2-a400-c711df13a450 · outbound

This paper cites The Journal of Chemical Physics81(1), 511–519 (1984) https://doi.

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction The Journal of Chemical Physics81(1), 511–519 (1984) https://doi

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-15T20:05:03.983956Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e95f1f4-6bbb-4bbf-a446-7cefd8bb7eb2 · outbound

This paper cites Phys- ical Review A31(3), 1695–1697 (1985) https://doi.org/10.1103/PhysRevA.31.

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction Phys- ical Review A31(3), 1695–1697 (1985) https://doi.org/10.1103/PhysRevA.31

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-15T20:05:03.988374Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c64c143e-200f-4d8e-a7f9-1e2deeccb208 · outbound

This paper cites Physical Review Letters102(2), 020601 (2009) https://doi.org/10.1103/PhysRevLett.102.020601.

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction Physical Review Letters102(2), 020601 (2009) https://doi.org/10.1103/PhysRevLett.102.020601

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-15T20:05:03.992927Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 926dae48-6d5e-4a4b-94b4-f9004ce52c0b · outbound

This paper cites an unresolved cited work.

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction Unresolved cited work

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-15T20:05:03.997528Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d58c2c10-5cf5-425b-9637-00fb6e888526 · outbound

This paper cites The Bell System Tech- nical Journal37(1), 185–282 (1958) https://doi.org/10.1002/j.1538-7305.1958.

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction The Bell System Tech- nical Journal37(1), 185–282 (1958) https://doi.org/10.1002/j.1538-7305.1958

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-15T20:05:04.002350Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d3fe6b1a-a52d-45eb-8fe6-e3059bbe3995 · outbound

This paper cites Journal of Chemical Theory and Computation15(1), 448–455 (2019) https://doi.org/10.

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction Journal of Chemical Theory and Computation15(1), 448–455 (2019) https://doi.org/10

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:05:05.051292Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation bc669bce-9b2c-4fc0-8f64-0c415522cb28 · outbound

This paper cites Journal of Machine Learning Research12, 2825–2830 (2011).

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction Journal of Machine Learning Research12, 2825–2830 (2011)

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-15T20:05:04.012270Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f0f464b8-fbf1-4336-ada9-3d8415eb1277 · outbound

This paper cites Journal of Chemical Theory and Computation16(5), 3307–3315 (2020) https://doi.org/10.1021/acs.jctc.0c00126.

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction Journal of Chemical Theory and Computation16(5), 3307–3315 (2020) https://doi.org/10.1021/acs.jctc.0c00126

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Resolution: verified exact; doi, observed 2026-08-15T20:05:04.096016Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c6bc7359-9d8d-46a5-97ce-64341beaf1a0 · outbound

This paper cites Journal of Chemical Theory and Computation16(11), 7044–7060 (2020) https://doi.org/10.1021/acs.jctc.0c00877.

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction Journal of Chemical Theory and Computation16(11), 7044–7060 (2020) https://doi.org/10.1021/acs.jctc.0c00877

Reference 84

Resolution: verified exact; doi, observed 2026-08-15T20:05:04.080395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 34dde013-b53e-4200-8c4c-a6a5d5e1712b · outbound

This paper cites International Journal of Computer Vision40(2), 99–121 (2000) https://doi.org/10.1023/A:1026543900054.

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction International Journal of Computer Vision40(2), 99–121 (2000) https://doi.org/10.1023/A:1026543900054

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-15T20:05:04.026539Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 23a29795-9cd1-419b-a0fd-3d3bcf4cb823 · outbound

This paper cites an unresolved cited work.

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction Unresolved cited work

Reference 86

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:05:05.027789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 18e3b0bd-734c-4b25-a290-226e0e73cd4c · outbound

This paper cites https://doi.org/10.1007/978-3-319-92955-2_9.

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction https://doi.org/10.1007/978-3-319-92955-2_9

Reference 344

Resolution: verified exact; doi, observed 2026-08-15T20:05:04.777185Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers