Materials Informatics III
Polymers, Solvents and Energetic Materials
Series: Challenges and Advances in Computational Chemistry and Physics; 41;
- Publisher's listprice EUR 213.99
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90 774 Ft (86 451 Ft + 5% VAT)
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90 774 Ft
Availability
Estimated delivery time: In stock at the publisher, but not at Prospero's office. Delivery time approx. 3-5 weeks.
Not in stock at Prospero.
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Delivery time is estimated on our previous experiences. We give estimations only, because we order from outside Hungary, and the delivery time mainly depends on how quickly the publisher supplies the book. Faster or slower deliveries both happen, but we do our best to supply as quickly as possible.
Product details:
- Publisher Springer
- Date of Publication 2 March 2025
- Number of Volumes 1 pieces, Book
- ISBN 9783031787232
- Binding Hardback
- No. of pages371 pages
- Size 235x155 mm
- Language English
- Illustrations 66 Illustrations, black & white; 42 Illustrations, color 0
Categories
Short description:
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.
MoreLong description:
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.
MoreTable of Contents:
Part 1. Introduction.- Introduction to Machine Learning for Predictive Modeling II.- Introduction to predicting properties of organic materials.- Part 2. Cheminformatic and Machine Learning Models for Polymers.- Machine Learning Applications in Polymer Informatics ? An Overview.- Applications of predictive modeling for selected properties of polymers.- Polymer Property Prediction using Machine Learning.- Applications of predictive modeling for polymers.- Part 3. Cheminformatic and Machine Learning Models for Solvents.- Applications of predictive QSPR modeling for deep eutectic solvents.- Applications of predictive modeling for various properties of ionic liquids.- Part 4. Cheminformatic and Machine Learning Models for Energetic Materials.- Improving Safety with Molecular-Scale Computational Approaches for Energetic and Reactive Materials.- Predictive modeling for energetic materials.- Modeling the performance of energetic materials.- Applications of predictive modeling for energetic materials.
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