Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials | Paper Citation Record

Observation 5620b5bc-5787-4d97-8c8e-2af9dfc1c74a · outbound

This paper cites A review of emerging non-volatile memory (NVM) technologies and applications.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials A review of emerging non-volatile memory (NVM) technologies and applications

Reference 1

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Observation c4d8299a-b8b7-4e7e-ac5a-72d0fc69d62e · outbound

This paper cites Ovonic threshold switching selectors for three-dimensional stackable phase-change memory.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials Ovonic threshold switching selectors for three-dimensional stackable phase-change memory

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:24.357288Z

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Observation d9277784-2250-4162-a850-cd394a0c00f0 · outbound

This paper cites Phase change materials and phase change memory.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials Phase change materials and phase change memory

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:24.168479Z

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Observation 7f487bb8-979b-44b3-8b3c-f0b4c5cb9d2c · outbound

This paper cites L.; Kundu, S.; Hody, H.; Devulder, W.; Houdt, J.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials L.; Kundu, S.; Hody, H.; Devulder, W.; Houdt, J

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:24.027002Z

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Observation ac1436af-7f4e-49f3-9a0a-13f64b8e9333 · outbound

This paper cites L.; Kundu, S.; Hody, H.; Devulder, W.; Houdt, J.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials L.; Kundu, S.; Hody, H.; Devulder, W.; Houdt, J

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:23.882174Z

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Observation 650a797b-9473-40c1-b727-9a81140a58ad · outbound

This paper cites an unresolved cited work.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-08-06T23:49:23.731104Z

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Observation c8c44ca2-d798-4a64-b159-6e4ffee91c4f · outbound

This paper cites Chalcogenide ovonic threshold switching selector.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials Chalcogenide ovonic threshold switching selector

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:23.605395Z

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Observation ea475d20-3254-4934-84f6-79ad4a3f6686 · outbound

This paper cites Phase-change materials for non-volatile memory devices: from technological challenges to materials science issues.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials Phase-change materials for non-volatile memory devices: from technological challenges to materials science issues

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:23.490506Z

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Observation 01c3ca63-89c3-4677-9444-4a58f16ff5f5 · outbound

This paper cites an unresolved cited work.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials Unresolved cited work

Reference 9

Resolution: unresolved; raw_fallback, observed 2026-08-06T23:49:23.341418Z

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Observation 0bc1455b-0356-487a-8d1f-81787ce04f1b · outbound

This paper cites L.; Witters, T.; Kundu, S.; Goux, L.; Afanasiev, V.; Kar, G.; Pourtois, G.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials L.; Witters, T.; Kundu, S.; Goux, L.; Afanasiev, V.; Kar, G.; Pourtois, G

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:23.186449Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b03a3146-79c8-4543-8ec3-6af196bbf865 · outbound

This paper cites GeSe ovonic threshold switch: the impact of functional layer thickness and device size.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials GeSe ovonic threshold switch: the impact of functional layer thickness and device size

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:23.017326Z

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Observation bd2e7af9-c547-4cdc-96e0-dd5bb5cfb630 · outbound

This paper cites S.; Lim, H.; Park, G.-H.; Hwang, C.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials S.; Lim, H.; Park, G.-H.; Hwang, C

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:22.825182Z

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Observation 1433a2f3-1928-4711-aeea-a6a4a2bd1fae · outbound

This paper cites Extended endurance performance and reduced threshold voltage by doping Si in GeSe-based ovonic threshold switching selectors.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials Extended endurance performance and reduced threshold voltage by doping Si in GeSe-based ovonic threshold switching selectors

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:22.609509Z

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Observation 1451aa85-b9c1-4f5a-a554-79dac4474b81 · outbound

This paper cites S.; Detavernier, C.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials S.; Detavernier, C

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:22.382433Z

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Observation 92d27bae-f89b-4de0-b593-15771eda65e4 · outbound

This paper cites V.; Karpov, V.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials V.; Karpov, V

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:22.207828Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1d5c9386-43ce-475b-bc77-a1dd8db1a6fe · outbound

This paper cites Analytical model for subthreshold conduction and threshold switching in chalcogenide-based memory devices.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials Analytical model for subthreshold conduction and threshold switching in chalcogenide-based memory devices

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:22.121601Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation eb897eae-a1e1-48d6-b166-90184d214450 · outbound

This paper cites A HydroDynamic model for trap-assisted tunneling conduction in ovonic devices.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials A HydroDynamic model for trap-assisted tunneling conduction in ovonic devices

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:21.984126Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d3d5dbff-f0b9-4a89-a172-0e02c801ec47 · outbound

This paper cites Current-driven threshold switching of a small polaron semiconductor to a metastable conductor.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials Current-driven threshold switching of a small polaron semiconductor to a metastable conductor

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:21.757143Z

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Observation b0f86b31-a089-452d-a887-76ebd54cda0c · outbound

This paper cites an unresolved cited work.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials Unresolved cited work

Reference 19

Resolution: unresolved; raw_fallback, observed 2026-08-06T23:49:21.504522Z

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Observation 52a7efb4-2cc1-494d-96f2-4906ff550ceb · outbound

This paper cites A unified model of nucleation switching.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials A unified model of nucleation switching

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:21.259426Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1bfd2d49-33d5-4a4b-9298-6a5ca88556ca · outbound

This paper cites Amorphous chalcogenide semiconductors and related materials, 2nd ed.; Springer Cham, 2021.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials Amorphous chalcogenide semiconductors and related materials, 2nd ed.; Springer Cham, 2021

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:21.123368Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1a13da09-5f5b-4ae2-b52b-9cba44ebdc15 · outbound

This paper cites G.; Strom, U.; Taylor, P.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials G.; Strom, U.; Taylor, P

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:20.956419Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 22a125b5-2ed6-4055-a897-705b1f5b27e0 · outbound

This paper cites Low temperature photoluminescence and fatigue effects in Se Ge glasses.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials Low temperature photoluminescence and fatigue effects in Se Ge glasses

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:20.839045Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c48e328b-7d19-48e4-9df7-d58cbae09f9b · outbound

This paper cites A.; Koóos, M.; Somogyi, I.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials A.; Koóos, M.; Somogyi, I

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:20.647741Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 92ee52c5-8370-424f-beb3-b0780ef3bf87 · outbound

This paper cites an unresolved cited work.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials Unresolved cited work

Reference 25

Resolution: unresolved; raw_fallback, observed 2026-08-06T23:49:20.477922Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 650091a7-6c23-4e77-b2e5-a2f7fa05a27d · outbound

This paper cites I.; Inuishi, Y.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials I.; Inuishi, Y

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:20.323938Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 149b9e66-5f4f-43ec-8a74-e6332e94da79 · outbound

This paper cites Valence-alternation model for localized gap states in lone-pair semiconductors.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials Valence-alternation model for localized gap states in lone-pair semiconductors

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:20.193761Z

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Observation d814c3ce-5d3f-4451-a0b6-c7013ce04cab · outbound

This paper cites S.; Silver, M.; Ovshinsky, S.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials S.; Silver, M.; Ovshinsky, S

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:20.062113Z

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Observation 46e689a1-79bd-4a00-a775-bb0ded56b883 · outbound

This paper cites Deep machine learning unravels the structural origin of mid-gap states in chalcogenide glass for high-density memory integration.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials Deep machine learning unravels the structural origin of mid-gap states in chalcogenide glass for high-density memory integration

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:19.957002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0f2a0717-4a24-4807-885d-c07a417a20fb · outbound

This paper cites Material relaxation in chalcogenide OTS selector materials.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials Material relaxation in chalcogenide OTS selector materials

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:19.829539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9c82a164-c816-48f8-99b2-16613793b09b · outbound

This paper cites Sb‐Se‐based electrical switching device with fast transition speed and minimized performance degradation due to stable mid‐gap states.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials Sb‐Se‐based electrical switching device with fast transition speed and minimized performance degradation due to stable mid‐gap states

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:19.691657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 77dd60a1-75c9-41f2-8f37-fe3f15c86ba2 · outbound

This paper cites Device‐to‐materials pathway for electron traps detection in amorphous GeSe‐based selectors.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials Device‐to‐materials pathway for electron traps detection in amorphous GeSe‐based selectors

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:19.535718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 29222233-e866-4eca-b762-a84a49080e59 · outbound

This paper cites Crystallization of amorphous GeTe simulated by neural network potential addressing medium-range order.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials Crystallization of amorphous GeTe simulated by neural network potential addressing medium-range order

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:19.383634Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b173ddd7-5831-46a5-abaa-9991c0078d3e · outbound

This paper cites C.; Deringer, V.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials C.; Deringer, V

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:19.268297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 40542854-aa67-4b26-a528-0a6f6fe30969 · outbound

This paper cites C.; Bernasconi, M.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials C.; Bernasconi, M

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:19.115348Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 700e3c90-3ba9-45ba-ba48-8af062def7a8 · outbound

This paper cites C.; Konstantinou, K.; Lee, T.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials C.; Konstantinou, K.; Lee, T

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:18.833317Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1012d5d7-2830-45e5-8b7e-3eaace5becb4 · outbound

This paper cites Quench-rate and size-dependent behaviour in glassy Ge_ 2 Sb_ 2 Te_ 5 models simulated with a machine-learned Gaussian approximation potential.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials Quench-rate and size-dependent behaviour in glassy Ge_ 2 Sb_ 2 Te_ 5 models simulated with a machine-learned Gaussian approximation potential

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:18.569823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c1d8d933-913a-4840-bae3-901a2a926932 · outbound

This paper cites L.; Zhang, W.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials L.; Zhang, W

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:18.288584Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f4de2bf2-0ec1-46af-a48f-6910d04d2169 · outbound

This paper cites P.; Shimamura, K.; Fukushima, S.; Shimojo, F.; Kalia, R.; Nakano, A.; Vashishta, P.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials P.; Shimamura, K.; Fukushima, S.; Shimojo, F.; Kalia, R.; Nakano, A.; Vashishta, P

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:17.996900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2220ed30-186a-47cf-a893-e303c1ffc57d · outbound

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

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials Generalized neural-network representation of high-dimensional potential-energy surfaces

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-06T23:49:07.577820Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e445175b-c27e-4cba-b2d4-0c587e0b27f0 · outbound

This paper cites an unresolved cited work.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials Unresolved cited work

Reference 41

Resolution: unresolved; raw_fallback, observed 2026-08-06T23:49:17.748631Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d5efdfab-ba3d-4b37-9f87-cb444a7217de · outbound

This paper cites Scalable parallel algorithm for graph neural network interatomic potentials in molecular dynamics simulations.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials Scalable parallel algorithm for graph neural network interatomic potentials in molecular dynamics simulations

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:17.539730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e2ff6fa2-ba86-40f1-ba60-fc8296eb145a · outbound

This paper cites A generalizable, uncertainty-aware neural network potential for GeSbTe with Monte Carlo dropout.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials A generalizable, uncertainty-aware neural network potential for GeSbTe with Monte Carlo dropout

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:17.254817Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e0823ab8-7509-4064-bcea-91b0b5bbea3b · outbound

This paper cites M.; Jang, J.; Yoon, S.; Ghosh, A.; Kim, M.; Kim, J.; Na, W.; Kim, J.; Choi, H.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials M.; Jang, J.; Yoon, S.; Ghosh, A.; Kim, M.; Kim, J.; Na, W.; Kim, J.; Choi, H

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:16.993522Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0bc685fa-cad8-484a-87a4-9209c53ca4c0 · outbound

This paper cites Change of short-range order with temperature and composition in liquid Ge_ x Se_ 1-x as shown by density measurements.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials Change of short-range order with temperature and composition in liquid Ge_ x Se_ 1-x as shown by density measurements

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:16.685000Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4ea2cd69-a888-4e69-aafc-f67561f304e1 · outbound

This paper cites Atomic energy mapping of neural network potential.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials Atomic energy mapping of neural network potential

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:16.445271Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ff49483c-e8d3-41f1-8a75-6357da6b9f3b · outbound

This paper cites P.; Kornbluth, M.; Molinari, N.; Smidt, T.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials P.; Kornbluth, M.; Molinari, N.; Smidt, T

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:16.254106Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e13b5a2b-6dd2-4a27-98fb-5431653e4903 · outbound

This paper cites P.; Simm, G.; Ortner, C.; Cs \'a nyi, G.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials P.; Simm, G.; Ortner, C.; Cs \'a nyi, G

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:16.032743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 47f6ea27-6b8e-4c0a-a1e4-ee5739ae35c7 · outbound

This paper cites SIMPLE-NN: an efficient package for training and executing neural-network interatomic potentials.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials SIMPLE-NN: an efficient package for training and executing neural-network interatomic potentials

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:15.806325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6fda987f-08b8-4106-96cb-d8c24cb81095 · outbound

This paper cites Atom-centered symmetry functions for constructing high-dimensional neural network potentials.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials Atom-centered symmetry functions for constructing high-dimensional neural network potentials

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-06T23:49:08.272683Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf3e08d9-7014-4a6a-b4a9-4312f6c9e1cc · outbound

This paper cites Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-06T23:49:08.323930Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 859c0d9d-3920-4c58-8f81-17a615da1404 · outbound

This paper cites an unresolved cited work.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials Unresolved cited work

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-06T23:49:08.405201Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 30c9718a-5e08-4e04-a9f2-6338a35a921d · outbound

This paper cites P.; Burke, K.; Ernzerhof, M.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials P.; Burke, K.; Ernzerhof, M

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-06T23:49:08.473380Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5b7345f5-0ce3-479a-9a4d-870b8a546da3 · outbound

This paper cites Mean-value point in the Brillouin zone.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials Mean-value point in the Brillouin zone

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:15.505441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ad9ba245-9906-4052-aa2c-bb6b5791322a · outbound

This paper cites P.; Levy, M.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials P.; Levy, M

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:15.248831Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7906c828-50df-4b57-a8a2-a595a9a7b296 · outbound

This paper cites Enhanced photocatalytic activity for water splitting of blue-phase GeS and GeSe monolayers via biaxial straining.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials Enhanced photocatalytic activity for water splitting of blue-phase GeS and GeSe monolayers via biaxial straining

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:15.008484Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8e524bd4-71dc-49af-aec9-51c4897ffa24 · outbound

This paper cites Atomistic structure of band-tail states in amorphous silicon.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials Atomistic structure of band-tail states in amorphous silicon

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:14.723593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4e1c75e3-5d6e-422d-800c-95238ff0c0b6 · outbound

This paper cites T.; Frost, J.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials T.; Frost, J

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:14.473464Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fc460df0-5f83-4167-b05f-b977c107e7c4 · outbound

This paper cites L.; Wei, S.-H.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials L.; Wei, S.-H

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:14.301876Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7e4c6a41-a787-44f3-9b05-2a947c0fd50b · outbound

This paper cites Amorphous and liquid semiconductors; Springer: New York, 1974; pp 159--220.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials Amorphous and liquid semiconductors; Springer: New York, 1974; pp 159--220

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:14.000215Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1c497396-a8d2-46da-ad5a-186df0dcd798 · outbound

This paper cites P.; Musaelian, A.; Simm, G.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials P.; Musaelian, A.; Simm, G

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:13.790057Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4289210d-bf4a-4716-a435-003d1356269f · outbound

This paper cites Ring statistics analysis of topological networks: new approach and application to amorphous GeS_ 2 and SiO_ 2 systems.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials Ring statistics analysis of topological networks: new approach and application to amorphous GeS_ 2 and SiO_ 2 systems

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:13.594763Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 391f259f-70b5-4e96-8264-41497731792e · outbound

This paper cites Understanding over-squashing and bottlenecks on graphs via curvature.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials Understanding over-squashing and bottlenecks on graphs via curvature

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-06T23:49:09.126827Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b83413cb-c990-4055-95cd-f857284a9675 · outbound

This paper cites Optical and electrical properties of GeSe and SnSe single crystals.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials Optical and electrical properties of GeSe and SnSe single crystals

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:13.321423Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 853f25b9-5d01-4aac-87dd-ee4c6a51d0d7 · outbound

This paper cites M.; Biacchi, A.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials M.; Biacchi, A

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:13.038427Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0d86f2ac-8407-4375-802f-99c6cbc12b10 · outbound

This paper cites an unresolved cited work.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials Unresolved cited work

Reference 66

Resolution: unresolved; raw_fallback, observed 2026-08-06T23:49:12.700355Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 188f213f-2863-43c4-b6cc-cc4ecf623373 · outbound

This paper cites Controllable threshold voltage (Vth) drift in ovonic threshold switch devices under a high-frequency continuous operation.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials Controllable threshold voltage (Vth) drift in ovonic threshold switch devices under a high-frequency continuous operation

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:12.441304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5620d1c7-babf-4873-984c-d98472179aca · outbound

This paper cites an unresolved cited work.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials Unresolved cited work

Reference 68

Resolution: unresolved; raw_fallback, observed 2026-08-06T23:49:12.162378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ba269187-2aaf-4256-bb4c-5124cddd568e · outbound

This paper cites an unresolved cited work.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials Unresolved cited work

Reference 69

Resolution: unresolved; raw_fallback, observed 2026-08-06T23:49:11.934768Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4bdbc707-cce9-4c1e-8580-4d8903f61100 · outbound

This paper cites u tt, Kristof T and Sauceda, Huziel E and Kindermans, P-J and Tkatchenko, Alexandre and M \.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials u tt, Kristof T and Sauceda, Huziel E and Kindermans, P-J and Tkatchenko, Alexandre and M \

Reference 70

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T23:49:11.626282Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8d511f54-98b6-4f15-9dfc-7f7aa0ac2e6c · outbound

This paper cites write newline.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials write newline

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-06T23:49:09.993188Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 43fbb858-77ce-40aa-b3c2-fccb8ec87951 · outbound

This paper cites , " * write output.state after.block = add.period write newline.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials , " * write output.state after.block = add.period write newline

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-06T23:49:10.126242Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2cf3b2aa-89fb-46a0-b736-1aa8eacbb5be · outbound

This paper cites write newline.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials write newline

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-06T23:49:10.301263Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ff8cb26-4e04-493b-994c-7539d5b77652 · outbound

This paper cites , " * write output.state after.block = add.period write newline.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials , " * write output.state after.block = add.period write newline

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-06T23:49:10.416170Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 56332802-7c67-4aa9-9b44-90c6230f9935 · outbound

This paper cites write newline.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials write newline

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-06T23:49:10.514125Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b78457c2-df58-414a-9fa8-f6629b5998e0 · outbound

This paper cites write newline.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials write newline

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-06T23:49:10.626479Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dbfe3719-3122-4978-96c5-bb495d425357 · outbound

This paper cites write newline.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials write newline

Reference 77

Resolution: malformed identifier; no resolver link, observed 2026-08-06T23:49:10.775697Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 51cefbbc-56f0-44c3-a2d6-732e7cb67325 · outbound

This paper cites , " * write output.state after.block = add.period write newline.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials , " * write output.state after.block = add.period write newline

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-06T23:49:10.887222Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9e8e5551-492b-4d17-87de-32b274123e06 · outbound

This paper cites write newline.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials write newline

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-06T23:49:11.035612Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f29e9317-fb9f-456a-8d5d-0191e56b730c · outbound

This paper cites Available from:.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials Available from:

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-06T23:49:11.142571Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ec2f1a7d-c580-4e82-8dae-1632836fb22b · outbound

This paper cites write newline.

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials write newline

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-06T23:49:11.294516Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Reference resolution

Outbound references

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