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Paper Citation Record · LEDGER

Analysis of degradation in perovskite solar cells through physics-based machine learning

As of 16 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 0 inbound Pith citation observations for arXiv:2608.10691.

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

pith.paper-citation-record.v1
2608.10691 v1

Coverage vector

measured 57 of 57 reference resolution

Typed states for the displayed outbound observations.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

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Reference resolution

57 of 57 outbound references displayed

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External citation measurements

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Outbound references

Observation de7d10cb-7095-4508-b056-ac78d4e76ac6 · outbound

This paper cites De rerum natura: How do halide perovskites self-heal from damage?Advanced Materials, 38(21):e18808, 2026.

Analysis of degradation in perovskite solar cells through physics-based machine learning De rerum natura: How do halide perovskites self-heal from damage?Advanced Materials, 38(21):e18808, 2026

Reference 1

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Observation 1b985a79-fbc6-4a55-ad02-146fdbf9f049 · outbound

This paper cites Roadmap on established and emerging photovoltaics for sustainable energy conversion.J.

Analysis of degradation in perovskite solar cells through physics-based machine learning Roadmap on established and emerging photovoltaics for sustainable energy conversion.J

Reference 2

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Observation 60c8549b-12bd-47d1-bb1b-35bb31367861 · outbound

This paper cites Eperon, Alessandro Virtuani, Quentin Jeangros, Dana B.

Analysis of degradation in perovskite solar cells through physics-based machine learning Eperon, Alessandro Virtuani, Quentin Jeangros, Dana B

Reference 3

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Observation 053a9c81-b989-4331-bf66-dd69479df7b8 · outbound

This paper cites Ion migration in perovskite solar cells.Nature Reviews Chemistry, pages 1–17, 2026.

Analysis of degradation in perovskite solar cells through physics-based machine learning Ion migration in perovskite solar cells.Nature Reviews Chemistry, pages 1–17, 2026

Reference 4

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 623d12c8-32e0-444e-8637-7f010f3b2437 · outbound

This paper cites Nonradiative re- combination in perovskite solar cells: the role of interfaces.Advanced Materials, 31(52):1902762, 2019.

Analysis of degradation in perovskite solar cells through physics-based machine learning Nonradiative re- combination in perovskite solar cells: the role of interfaces.Advanced Materials, 31(52):1902762, 2019

Reference 5

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Observation 508b989d-2d97-4896-85f1-3d2ce044675a · outbound

This paper cites an unresolved cited work.

Analysis of degradation in perovskite solar cells through physics-based machine learning Unresolved cited work

Reference 6

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Observation a32eb729-b511-44bc-9b00-2825eaeb22de · outbound

This paper cites The impact of interfacial quality and nanoscale performance disorder on the stability of alloyed perovskite solar cells.Nature Energy, 10(1):66–76, 2025.

Analysis of degradation in perovskite solar cells through physics-based machine learning The impact of interfacial quality and nanoscale performance disorder on the stability of alloyed perovskite solar cells.Nature Energy, 10(1):66–76, 2025

Reference 7

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Observation 0c7f49a1-6bec-4ade-b50d-d8dc37e7e89c · outbound

This paper cites Challenges and opportunities for the characterization of electronic properties in halide perovskite solar cells.Chemical Science, 16(19):8153–8195, 2025.

Analysis of degradation in perovskite solar cells through physics-based machine learning Challenges and opportunities for the characterization of electronic properties in halide perovskite solar cells.Chemical Science, 16(19):8153–8195, 2025

Reference 8

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Source-reported events for the cited work

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Observation 1cb4def9-d066-4c16-b83b-f6af17824081 · outbound

This paper cites How halide segregation governs the ion density evolution and ionic performance losses: From degradation to recovery.Advanced Energy Materials, page e03866, 2026.

Analysis of degradation in perovskite solar cells through physics-based machine learning How halide segregation governs the ion density evolution and ionic performance losses: From degradation to recovery.Advanced Energy Materials, page e03866, 2026

Reference 9

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Observation b82f6e50-1e6d-4f48-908d-d42299c763a1 · outbound

This paper cites an unresolved cited work.

Analysis of degradation in perovskite solar cells through physics-based machine learning Unresolved cited work

Reference 10

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Observation cabfccfe-9f00-4717-92fc-9f90581f48f4 · outbound

This paper cites O’Regan, and Piers R.F.

Analysis of degradation in perovskite solar cells through physics-based machine learning O’Regan, and Piers R.F

Reference 11

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Observation 66367a89-05c1-410e-bda3-99d799f8c3e8 · outbound

This paper cites Ion-induced field screening as a dominant factor in perovskite solar cell operational stability.Nature Energy, 9:1–13, 2024.

Analysis of degradation in perovskite solar cells through physics-based machine learning Ion-induced field screening as a dominant factor in perovskite solar cell operational stability.Nature Energy, 9:1–13, 2024

Reference 12

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Observation efae784e-83fd-40e8-8cdb-cc44982ca37b · outbound

This paper cites an unresolved cited work.

Analysis of degradation in perovskite solar cells through physics-based machine learning Unresolved cited work

Reference 13

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Observation f3f2410f-0819-472f-92bf-44c916ca6651 · outbound

This paper cites Torre Cachafeiro and W Tress.

Analysis of degradation in perovskite solar cells through physics-based machine learning Torre Cachafeiro and W Tress

Reference 14

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Observation cc702afe-af64-4e75-a5ae-49c089094e28 · outbound

This paper cites The curse(s) of dimensionality.Nat Methods, 15(15):399–400, 2018.

Analysis of degradation in perovskite solar cells through physics-based machine learning The curse(s) of dimensionality.Nat Methods, 15(15):399–400, 2018

Reference 15

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Observation 6ea385ba-317b-4651-b702-7903ebdca84b · outbound

This paper cites Bayesian parameter estimation for characterising mobile ion vacancies in perovskite solar cells.Journal of Physics: Energy, 6:015005, 2023.

Analysis of degradation in perovskite solar cells through physics-based machine learning Bayesian parameter estimation for characterising mobile ion vacancies in perovskite solar cells.Journal of Physics: Energy, 6:015005, 2023

Reference 16

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Observation cdc53913-c623-4295-b2e0-e546f6caae97 · outbound

This paper cites Taking control of ion transport in halide perovskite solar cells.ACS Energy Letters, 3(8):1983–1990, 2018.

Analysis of degradation in perovskite solar cells through physics-based machine learning Taking control of ion transport in halide perovskite solar cells.ACS Energy Letters, 3(8):1983–1990, 2018

Reference 17

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Observation e254c620-ea64-4845-9185-cea53453e493 · outbound

This paper cites De Souza.

Analysis of degradation in perovskite solar cells through physics-based machine learning De Souza

Reference 18

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Observation 6199108d-19f6-4ddb-bee2-dcedade9791f · outbound

This paper cites Mocanu, and M.

Analysis of degradation in perovskite solar cells through physics-based machine learning Mocanu, and M

Reference 19

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Observation 4040c4a5-500b-4911-8845-1b8544e9a7aa · outbound

This paper cites American Chemical Society, 2022.

Analysis of degradation in perovskite solar cells through physics-based machine learning American Chemical Society, 2022

Reference 20

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Observation 679f8d3b-335c-49ff-970c-72f3be1f4723 · outbound

This paper cites Rombach, David P.

Analysis of degradation in perovskite solar cells through physics-based machine learning Rombach, David P

Reference 21

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Observation 020e08a2-1c0f-4d56-9303-c0d1f9bc3b9d · outbound

This paper cites Rapid parameter prediction in perovskite solar cells via ai-assisted performance analysis.ACS Energy Letters, 2026.

Analysis of degradation in perovskite solar cells through physics-based machine learning Rapid parameter prediction in perovskite solar cells via ai-assisted performance analysis.ACS Energy Letters, 2026

Reference 22

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Observation 0732681a-1767-4030-98f8-db163ef96ef2 · outbound

This paper cites Inversion of the impedance response towards physical parameter extraction using interpretable machine learning.Advanced Energy Materials, page e06352, 2026.

Analysis of degradation in perovskite solar cells through physics-based machine learning Inversion of the impedance response towards physical parameter extraction using interpretable machine learning.Advanced Energy Materials, page e06352, 2026

Reference 23

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Observation e6b89563-11f3-41e7-a34e-c4db00e60087 · outbound

This paper cites Bayesian optimization approach to quantify the effect of input parameter uncertainty on predictions of numerical physics simulations.APL Machine Learning, 1(4), 2023.

Analysis of degradation in perovskite solar cells through physics-based machine learning Bayesian optimization approach to quantify the effect of input parameter uncertainty on predictions of numerical physics simulations.APL Machine Learning, 1(4), 2023

Reference 24

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Observation bd7e418b-504d-46ff-8647-4ba872bd6ac8 · outbound

This paper cites Rayflare: flexible optical modelling of solar cells.Journal of Open Source Software, 6(65):3460, 2021.

Analysis of degradation in perovskite solar cells through physics-based machine learning Rayflare: flexible optical modelling of solar cells.Journal of Open Source Software, 6(65):3460, 2021

Reference 25

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Observation 3d6ba263-5828-44da-911c-c7706c008802 · outbound

This paper cites an unresolved cited work.

Analysis of degradation in perovskite solar cells through physics-based machine learning Unresolved cited work

Reference 26

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation c6d57633-bf46-4a77-bf64-bef2e8553ca0 · outbound

This paper cites an unresolved cited work.

Analysis of degradation in perovskite solar cells through physics-based machine learning Unresolved cited work

Reference 27

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Observation 664d7e6b-8696-4d8f-a5d6-8bec33c6555e · outbound

This paper cites Inverted hysteresis as a diagnostic tool for perovskite solar cells: Insights from the drift-diffusion model.Journal of Applied Physics, 133(9), 2023.

Analysis of degradation in perovskite solar cells through physics-based machine learning Inverted hysteresis as a diagnostic tool for perovskite solar cells: Insights from the drift-diffusion model.Journal of Applied Physics, 133(9), 2023

Reference 28

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Observation b80c760e-628b-44cd-a8bd-a875ef8ea8e7 · outbound

This paper cites an unresolved cited work.

Analysis of degradation in perovskite solar cells through physics-based machine learning Unresolved cited work

Reference 29

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Observation e7462fe2-fc51-4fe3-a2e8-89d446b5fc19 · outbound

This paper cites Understanding the full zoo of perovskite so- lar cell impedance spectra with the standard drift-diffusion model.Advanced Energy Materials, page 2400955, 2024.

Analysis of degradation in perovskite solar cells through physics-based machine learning Understanding the full zoo of perovskite so- lar cell impedance spectra with the standard drift-diffusion model.Advanced Energy Materials, page 2400955, 2024

Reference 30

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Observation b003bc83-3344-4db7-a27b-0e96ec09e841 · outbound

This paper cites an unresolved cited work.

Analysis of degradation in perovskite solar cells through physics-based machine learning Unresolved cited work

Reference 31

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Observation 64ec226b-9ebc-4bb3-9f4f-bccd9018fb8a · outbound

This paper cites CreateSpace, United States, 3rd ed edition, 2013.

Analysis of degradation in perovskite solar cells through physics-based machine learning CreateSpace, United States, 3rd ed edition, 2013

Reference 32

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-12T19:24:31.548197Z digest=sha256:39bdd022803ab8b37db353efd8b32758d1428edb65220fbc7dc29b8805dfe0b0

Observation 16cb8766-69f4-447a-acb0-77d5dfc4176d · outbound

This paper cites Multifunctional dual-interface layer enables efficient and stable inverted perovskite solar cells.Physical Chemistry Chemical Physics, 26(10):8299–8307, 2024.

Analysis of degradation in perovskite solar cells through physics-based machine learning Multifunctional dual-interface layer enables efficient and stable inverted perovskite solar cells.Physical Chemistry Chemical Physics, 26(10):8299–8307, 2024

Reference 33

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raw_fallback, observed 2026-08-12T19:24:32.058861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-12T19:24:31.552780Z digest=sha256:50ac65a2678e68d90971f4027398cf3aa3dd4fe045d463c8dd46f99f10f835a8

Observation 105d6e82-ff86-4366-b123-2a1757532752 · outbound

This paper cites Solution-processed metal-oxide nanoparticles to prevent the sputtering damage in perovskite/silicon tandem solar cells.ACS Applied Materials & Interfaces, 17(11):17599–17610, 2025.

Analysis of degradation in perovskite solar cells through physics-based machine learning Solution-processed metal-oxide nanoparticles to prevent the sputtering damage in perovskite/silicon tandem solar cells.ACS Applied Materials & Interfaces, 17(11):17599–17610, 2025

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T19:24:32.043777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-12T19:24:31.557317Z digest=sha256:e2b073962f72afc7d47e19d4ac6e4879bd06dc2aa99761628e77219a18f5e608

Observation d0db3985-49c8-41ea-a4a3-ab5a60c839db · outbound

This paper cites Kauf- mann, and Aldo Di Carlo.

Analysis of degradation in perovskite solar cells through physics-based machine learning Kauf- mann, and Aldo Di Carlo

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T19:24:32.028843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-12T19:24:31.561580Z digest=sha256:4b38fdcd14064a598e2771d7a6c8c0d169a78dced9db142498853491f04a3bc0

Observation 9e5c7a0e-8702-4152-bb47-7c2a814a887c · outbound

This paper cites Hill, Matthew V.

Analysis of degradation in perovskite solar cells through physics-based machine learning Hill, Matthew V

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T19:24:32.013959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-12T19:24:31.565891Z digest=sha256:2892665c017a780d1b27e736e6a6b294cfe20f8b2479e4b1f4d3ede24c5f813a

Observation bae19d30-3058-4ef2-aeb8-c9cda296750c · outbound

This paper cites an unresolved cited work.

Analysis of degradation in perovskite solar cells through physics-based machine learning Unresolved cited work

Reference 37

Resolution
verified exact
doi, observed 2026-08-12T19:24:31.702947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-12T19:24:31.570557Z digest=sha256:b2d9384460b6cd51406b0ee0f7dd0e8f83c05ca1911fa5401c2e76552a70504c

Observation 4a41d316-8785-4cae-9e15-6d347d54cd7c · outbound

This paper cites an unresolved cited work.

Analysis of degradation in perovskite solar cells through physics-based machine learning Unresolved cited work

Reference 38

Resolution
unresolved
raw_fallback, observed 2026-08-12T19:24:31.998450Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-12T19:24:31.575938Z digest=sha256:d6690afcd2e2bb24bad108bac8a256a27cad2f6a1fb673ff80d04df5bf388de4

Observation 20866f2e-8b52-4a0a-9a09-40ef45e5e1af · outbound

This paper cites Cave, Nicola E.

Analysis of degradation in perovskite solar cells through physics-based machine learning Cave, Nicola E

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T19:24:31.983777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-12T19:24:31.580422Z digest=sha256:9085e8016095c346a0afa62e77c9a7eb6ce70de1e92b2c815fb8ca138ab6a7e3

Observation 1ac3147b-2571-4994-acc3-973094afb5a7 · outbound

This paper cites Understanding performance limiting interfacial recombination in pin perovskite solar cells.Advanced Energy Materials, 13:230313, 2023.

Analysis of degradation in perovskite solar cells through physics-based machine learning Understanding performance limiting interfacial recombination in pin perovskite solar cells.Advanced Energy Materials, 13:230313, 2023

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T19:24:31.969466Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-12T19:24:31.585278Z digest=sha256:e9f5a15fbda702b804970bde44db05c80e4f10d521da2e8efb52023ccae06801

Observation a90ed578-30ab-4a8f-8cb1-ee3cd3a7ce5f · outbound

This paper cites Rombach, Akash Dasgupta, Manuel Kober-Czerny, et al.

Analysis of degradation in perovskite solar cells through physics-based machine learning Rombach, Akash Dasgupta, Manuel Kober-Czerny, et al

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T19:24:31.954870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-12T19:24:31.590508Z digest=sha256:38be80d836eed7559782c5c7a48daec6700ac7622d34e519a356f7aa371866ab

Observation 5319c06a-60cd-4f74-84e0-f414537e90b3 · outbound

This paper cites Preventing phase segregation in mixed-halide per- ovskites: a perspective.Energy & Environmental Science, 13(7):2024–2046, 2020.

Analysis of degradation in perovskite solar cells through physics-based machine learning Preventing phase segregation in mixed-halide per- ovskites: a perspective.Energy & Environmental Science, 13(7):2024–2046, 2020

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T19:24:31.940610Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-12T19:24:31.595015Z digest=sha256:65fdd432d67a9b0f703b95412c8dc2579fa52617aea39d08ecb008298ce5b944

Observation 67d4093e-dcba-4331-ae34-cb3fca059ffc · outbound

This paper cites De Souza and Denis Barboni.

Analysis of degradation in perovskite solar cells through physics-based machine learning De Souza and Denis Barboni

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T19:24:31.926264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-12T19:24:31.599390Z digest=sha256:8347279fc5357cff1f9477cf9589b68f30827d081548ac4d4ab433a6766883a6

Observation 6965d9e8-601b-421e-b045-9ddc8f70145e · outbound

This paper cites Defect chemistry of mixed ionic–electronic conductors under light: halide perovskites as a master example.Mater.

Analysis of degradation in perovskite solar cells through physics-based machine learning Defect chemistry of mixed ionic–electronic conductors under light: halide perovskites as a master example.Mater

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T19:24:31.912160Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-12T19:24:31.603761Z digest=sha256:ab0e669f8ef22b6ec7dfc9faa4c2980beb99868dc627d14ceef85a15bf4af798

Observation 02bd5db4-3848-4907-87e7-eaae5d18ccd4 · outbound

This paper cites an unresolved cited work.

Analysis of degradation in perovskite solar cells through physics-based machine learning Unresolved cited work

Reference 45

Resolution
unresolved
raw_fallback, observed 2026-08-12T19:24:31.898594Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-12T19:24:31.608302Z digest=sha256:56633943994f54582f99a23315910584a15f45fe175b1454490e67d16c6821df

Observation a196d0bc-20f9-4fa7-9c9d-3ec75e802c72 · outbound

This paper cites Electrical conductivity of halide perovskites follows expectations from classical defect chemistry.European Journal of Inorganic Chemistry, 2021(28):2882–2889, 2021.

Analysis of degradation in perovskite solar cells through physics-based machine learning Electrical conductivity of halide perovskites follows expectations from classical defect chemistry.European Journal of Inorganic Chemistry, 2021(28):2882–2889, 2021

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T19:24:31.884819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-12T19:24:31.613448Z digest=sha256:002d0f2e042e264ce1cace9b9998fdcd089f8d337ff30fb83149a8d99e74ad77

Observation dfb00fd9-5df3-41e1-9884-8cb77dc3211d · outbound

This paper cites Toolsets for assessing ionic migration in halide per- ovskites.Joule, 8(5):1239–1273, 2024.

Analysis of degradation in perovskite solar cells through physics-based machine learning Toolsets for assessing ionic migration in halide per- ovskites.Joule, 8(5):1239–1273, 2024

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T19:24:31.871233Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-12T19:24:31.618604Z digest=sha256:69ad4f7eda4ef843e4c8db42f12f37e596256f5b64deea21f8592cc64c0e4a0e

Observation eadf57a2-ee34-4a53-bbbe-38ce3a704bd8 · outbound

This paper cites Effect of thermal stress on the ion density and mobility distribution in perovskite solar cells.The Journal of Physical Chemistry Letters, 2026.

Analysis of degradation in perovskite solar cells through physics-based machine learning Effect of thermal stress on the ion density and mobility distribution in perovskite solar cells.The Journal of Physical Chemistry Letters, 2026

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T19:24:31.857326Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-12T19:24:31.623693Z digest=sha256:2fbf7351269b6b351c6ea24f607f509355a6340febfeb3f55c95f1186976a8b6

Observation eaeb2ba9-d654-4fc9-b1a5-a103b6fbe631 · outbound

This paper cites Ionic-defect distribution revealed by improved evaluation of deep-level transient spectroscopy on perovskite solar cells.Physical Review Applied, 13(3):034018, 2020.

Analysis of degradation in perovskite solar cells through physics-based machine learning Ionic-defect distribution revealed by improved evaluation of deep-level transient spectroscopy on perovskite solar cells.Physical Review Applied, 13(3):034018, 2020

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T19:24:31.842653Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-12T19:24:31.628281Z digest=sha256:2115dc6f8f56095c94a2a075d67432d4c78adef66c5cc4fc548a347396f712d1

Observation 5d59e420-e6de-4d87-9b31-bc5ab49c693e · outbound

This paper cites How to tell the difference between a model and a digital twin.Advanced Modeling and Simulation in Engineering Sciences, 7:7–13, 2020.

Analysis of degradation in perovskite solar cells through physics-based machine learning How to tell the difference between a model and a digital twin.Advanced Modeling and Simulation in Engineering Sciences, 7:7–13, 2020

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T19:24:31.828219Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-12T19:24:31.632540Z digest=sha256:1a711628c46f79bda80f81fa9d7d0bc4e41425f7ccff4fde2305e9749521d8c9

Observation 696f61d5-3bee-4980-b18b-8e2eb58ec5c3 · outbound

This paper cites A fast and robust numerical scheme for solving models of charge carrier transport and ion vacancy motion in perovskite solar cells.

Analysis of degradation in perovskite solar cells through physics-based machine learning A fast and robust numerical scheme for solving models of charge carrier transport and ion vacancy motion in perovskite solar cells

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T19:24:31.813392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-12T19:24:31.636896Z digest=sha256:15920dc40d754203b84e0f6e30140c3160f589d08a3668a0aa1e6fd6e362c77d

Observation 6325e9bc-804d-4d04-b76f-6b93bdf8bd45 · outbound

This paper cites Substitution of lead with tin suppresses ionic transport in halide perovskite optoelectronics.

Analysis of degradation in perovskite solar cells through physics-based machine learning Substitution of lead with tin suppresses ionic transport in halide perovskite optoelectronics

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T19:24:31.798538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-12T19:24:31.641181Z digest=sha256:d4a516b349acc818a1bc673eac18e23ae7114ee87a62183b32b0d384a96a3951

Observation 3a6e818a-40a9-4bf4-852c-3b3c1c89aa5f · outbound

This paper cites Avoid pitfalls in identi- fying perovskite grain size.The journal of physical chemistry letters, 13(31):7236–7242, 2022.

Analysis of degradation in perovskite solar cells through physics-based machine learning Avoid pitfalls in identi- fying perovskite grain size.The journal of physical chemistry letters, 13(31):7236–7242, 2022

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T19:24:31.783116Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-12T19:24:31.645624Z digest=sha256:15933d12cf8d5f98a3376c58030e8841c9571705ed4264ab2962e4ee66dcb1bd

Observation fa503479-2ed6-401b-95d8-c469f8373ce7 · outbound

This paper cites Light management in perovskite photovoltaic solar cells: A perspective.

Analysis of degradation in perovskite solar cells through physics-based machine learning Light management in perovskite photovoltaic solar cells: A perspective

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T19:24:31.767196Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-12T19:24:31.649896Z digest=sha256:90f9403205b78b8379c7eca2b6aa9a4e008a8ecb6c3961853a86cc18d0af0989

Observation 54851721-2033-4440-a38a-3e55a1482cf5 · outbound

This paper cites an unresolved cited work.

Analysis of degradation in perovskite solar cells through physics-based machine learning Unresolved cited work

Reference 55

Resolution
unresolved
raw_fallback, observed 2026-08-12T19:24:31.751420Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-12T19:24:31.654310Z digest=sha256:73da57d42ef7303e25e32bff485cedc725bc6219341c005161862dad0413e7be

Observation 1f856b38-8832-470f-89f1-488f554ae865 · outbound

This paper cites Functionalized substrates for reduced nonradiative recombination in metal-halide per- ovskites.The Journal of Physical Chemistry Letters, 16(1):372–377, 2025.

Analysis of degradation in perovskite solar cells through physics-based machine learning Functionalized substrates for reduced nonradiative recombination in metal-halide per- ovskites.The Journal of Physical Chemistry Letters, 16(1):372–377, 2025

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T19:24:31.735065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-12T19:24:31.658874Z digest=sha256:7232434ceece7116d6be09c299015b7e8286707de9551d59571149aae2f246c9

Observation 97665eea-c58c-4b25-b206-f47063058915 · outbound

This paper cites 30 (a) (b) (c) Figure A.6: QFLS,qV oc and their difference, in eV, from open-circuit IonMonger runs for combina- tions of interface recombination velocity values.

Analysis of degradation in perovskite solar cells through physics-based machine learning 30 (a) (b) (c) Figure A.6: QFLS,qV oc and their difference, in eV, from open-circuit IonMonger runs for combina- tions of interface recombination velocity values

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T19:24:31.719357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-12T19:24:31.664566Z digest=sha256:39f52476be12f71362c42acd54a1ff811819f9d81d1a0a40660c89b834892a36

Pith citing papers

No inbound Pith citation observations are available.