Machine Learning Accelerated Prediction of Self‐Trapped Excitons in Double Halide Perovskites
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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Machine Learning Accelerated Prediction of Self‐Trapped Excitons in Double Halide Perovskites ; volume:4 ; number:12 ; year:2023 ; extent:1
Advanced energy & sustainability research ; 4, Heft 12 (2023) (gesamt 1)
- Creator
- DOI
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10.1002/aesr.202370024
- URN
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urn:nbn:de:101:1-2023120815041465449437
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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15.08.2025, 7:32 AM CEST
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Associated
- Chen, Baian
- Chen, Rui
- Huang, Bolong
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Strong decays of the newly $$P_cs}(4459)$$ P cs ( 4459 ) as a strange hidden-charm $$\Xi _c\bar{D}}^*$$ Ξ c D ¯ ∗ molecule
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Multi-level autonomous integrity monitoring method for multi-source PNT resilient fusion navigation
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Machine Learning‐Accelerated Development of Perovskite Optoelectronics Toward Efficient Energy Harvesting and Conversion
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Highly Emissive Self‐Trapped Excitons in Fully Inorganic Zero‐Dimensional Tin Halides
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Machine Learning Accelerated Prediction of Self‐Trapped Excitons in Double Halide Perovskites
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Strong decays of the newly $$P_cs}(4459)$$ P cs ( 4459 ) as a strange hidden-charm $$\Xi _c\bar{D}}^*$$ Ξ c D ¯ ∗ molecule
Self-trapped excitons
Self-trapped excitons
Multi-level autonomous integrity monitoring method for multi-source PNT resilient fusion navigation
Integrated analysis identifies oxidative stress-related lncRNAs associated with progression and prognosis in colorectal cancer
Machine Learning‐Accelerated Development of Perovskite Optoelectronics Toward Efficient Energy Harvesting and Conversion
Inwardly Rectifying Potassium (Kir) Channels Represent a Critical Ion Conductance Pathway in the Nervous Systems of Insects
Highly Emissive Self‐Trapped Excitons in Fully Inorganic Zero‐Dimensional Tin Halides