Inverse Design of Nanoparticles Using Multi‐Target Machine Learning
Abstract: In this study a new approach to inverse design is presented that draws on the multi‐functionality of nanomaterials and uses sets of properties to predict a unique nanoparticle structure. This approach involves multi‐target regression and uses a precursory forward structure/property prediction to focus the model on the most important characteristics before inverting the problem and simultaneously predicting multiple structural features of a single nanoparticle. The workflow is general, as demonstrated on two nanoparticle data sets, and can rapidly predict property/structure relationships to guide further research and development without the need for additional optimization or high‐throughput sampling.
- 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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Inverse Design of Nanoparticles Using Multi‐Target Machine Learning ; day:07 ; month:12 ; year:2021 ; extent:12
Advanced theory and simulations ; (07.12.2021) (gesamt 12)
- Creator
- DOI
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10.1002/adts.202100414
- URN
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urn:nbn:de:101:1-2021120814430730403761
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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15.08.2025, 7:37 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Li, Sichao
- Barnard, Amanda
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