Materials Informatics III Polymers, Solvents and Energetic Materials /
This contributed volume focuses on the application of machine learning and cheminformatics in predictive modeling for organic materials, polymers, solvents, and energetic materials. It provides an in-depth look at how machine learning is utilized to predict key properties of polymers, deep eutectic...
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| Altri autori: | , |
| Natura: | Elettronico eBook |
| Lingua: | inglese |
| Pubblicazione: |
Cham :
Springer Nature Switzerland : Imprint: Springer,
2025.
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| Edizione: | 1st ed. 2025. |
| Serie: | Challenges and Advances in Computational Chemistry and Physics,
41 |
| Soggetti: | |
| Accesso online: | https://doi.org/10.1007/978-3-031-78724-9 |
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| Riassunto: | This contributed volume focuses on the application of machine learning and cheminformatics in predictive modeling for organic materials, polymers, solvents, and energetic materials. It provides an in-depth look at how machine learning is utilized to predict key properties of polymers, deep eutectic solvents, and ionic liquids, as well as to improve safety and performance in the study of energetic and reactive materials. With chapters covering polymer informatics, quantitative structure-property relationship (QSPR) modeling, and computational approaches, the book serves as a comprehensive resource for researchers applying predictive modeling techniques to advance materials science and improve material safety and performance. |
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| Descrizione fisica: | XV, 371 p. 108 illus., 42 illus. in color. online resource. |
| ISBN: | 9783031787249 |
| ISSN: | 2542-4483 ; |
| DOI: | 10.1007/978-3-031-78724-9 |