Materials Informatics II Software Tools and Databases /
This contributed volume explores the application of machine learning in predictive modeling within the fields of materials science, nanotechnology, and cheminformatics. It covers a range of topics, including electronic properties of metal nanoclusters, carbon quantum dots, toxicity assessments of na...
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| Coauteur: | |
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| Andere auteurs: | , |
| Formaat: | Elektronisch E-boek |
| Taal: | Engels |
| Gepubliceerd in: |
Cham :
Springer Nature Switzerland : Imprint: Springer,
2025.
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| Editie: | 1st ed. 2025. |
| Reeks: | Challenges and Advances in Computational Chemistry and Physics,
40 |
| Onderwerpen: | |
| Online toegang: | https://doi.org/10.1007/978-3-031-78728-7 |
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| Samenvatting: | This contributed volume explores the application of machine learning in predictive modeling within the fields of materials science, nanotechnology, and cheminformatics. It covers a range of topics, including electronic properties of metal nanoclusters, carbon quantum dots, toxicity assessments of nanomaterials, and predictive modeling for fullerenes and perovskite materials. Additionally, the book discusses multiscale modeling and advanced decision support systems for nanomaterial risk management, while also highlighting various machine learning tools, databases, and web platforms designed to predict the properties of materials and molecules. It is a comprehensive guide and a great tool for researchers working at the intersection of machine learning and material sciences. |
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| Fysieke beschrijving: | XVI, 297 p. 102 illus., 95 illus. in color. online resource. |
| ISBN: | 9783031787287 |
| ISSN: | 2542-4483 ; |
| DOI: | 10.1007/978-3-031-78728-7 |