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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| Müşterek Yazar: | |
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| Diğer Yazarlar: | , |
| Materyal Türü: | Elektronik Ekitap |
| Dil: | İngilizce |
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Cham :
Springer Nature Switzerland : Imprint: Springer,
2025.
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| Edisyon: | 1st ed. 2025. |
| Seri Bilgileri: | Challenges and Advances in Computational Chemistry and Physics,
41 |
| Konular: | |
| Online Erişim: | https://doi.org/10.1007/978-3-031-78724-9 |
| Etiketler: |
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İçindekiler:
- 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.