9783031787263 - Materials Informatics III: Polymers, Solvents and Energetic Materials: 41 (4 risultati)

Taschenbuch. Condizione: Neu. Materials Informatics III | Polymers, Solvents and Energetic Materials | Kunal Roy (u. a.) | Taschenbuch | Challenges and Advances in Computational Chemistry and Physics | Englisch | 2026 | Springer | EAN 9783031787263 | Verantwortliche Person für die EU: Springer Nature Customer Service Center GmbH, Europaplatz 3, 69115 Heidelberg, productsafety[at]springernature[dot]com | Anbieter: preigu.

Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This contributed volumefocuses 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.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This contributed volumefocuses 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. 388 pp. Englisch.

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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 structureproperty 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. 388 pp. Englisch.