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...
Збережено в:
| Співавтор: | |
|---|---|
| Інші автори: | , |
| Формат: | Електронний ресурс eКнига |
| Мова: | Англійська |
| Опубліковано: |
Cham :
Springer Nature Switzerland : Imprint: Springer,
2025.
|
| Редагування: | 1st ed. 2025. |
| Серія: | Challenges and Advances in Computational Chemistry and Physics,
41 |
| Предмети: | |
| Онлайн доступ: | https://doi.org/10.1007/978-3-031-78724-9 |
| Теги: |
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| 245 | 1 | 0 | |a Materials Informatics III |h [electronic resource] : |b Polymers, Solvents and Energetic Materials / |c edited by Kunal Roy, Arkaprava Banerjee. |
| 250 | |a 1st ed. 2025. | ||
| 264 | 1 | |a Cham : |b Springer Nature Switzerland : |b Imprint: Springer, |c 2025. | |
| 300 | |a XV, 371 p. 108 illus., 42 illus. in color. |b online resource. | ||
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| 347 | |a text file |b PDF |2 rda | ||
| 490 | 1 | |a Challenges and Advances in Computational Chemistry and Physics, |x 2542-4483 ; |v 41 | |
| 505 | 0 | |a 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. | |
| 520 | |a 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. | ||
| 532 | 8 | |a Accessibility summary: This PDF does not fully comply with PDF/UA standards, but does feature limited screen reader support, described non-text content (images, graphs), bookmarks for easy navigation and searchable, selectable text. Users of assistive technologies may experience difficulty navigating or interpreting content in this document. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupport@springernature.com. | |
| 532 | 8 | |a No reading system accessibility options actively disabled | |
| 532 | 8 | |a Publisher contact for further accessibility information: accessibilitysupport@springernature.com | |
| 650 | 0 | |a Chemoinformatics. | |
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Materials. | |
| 650 | 0 | |a Catalysis. | |
| 650 | 0 | |a Force and energy. | |
| 650 | 0 | |a Materials science |x Data processing. | |
| 650 | 0 | |a Nanochemistry. | |
| 650 | 1 | 4 | |a Cheminformatics. |
| 650 | 2 | 4 | |a Machine Learning. |
| 650 | 2 | 4 | |a Materials for Energy and Catalysis. |
| 650 | 2 | 4 | |a Computational Materials Science. |
| 650 | 2 | 4 | |a Nanochemistry. |
| 700 | 1 | |a Roy, Kunal. |e editor. |0 (orcid)0000-0003-4486-8074 |1 https://orcid.org/0000-0003-4486-8074 |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Banerjee, Arkaprava. |e editor. |0 (orcid)0000-0001-8468-0784 |1 https://orcid.org/0000-0001-8468-0784 |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
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| 776 | 0 | 8 | |i Printed edition: |z 9783031787232 |
| 776 | 0 | 8 | |i Printed edition: |z 9783031787256 |
| 776 | 0 | 8 | |i Printed edition: |z 9783031787263 |
| 830 | 0 | |a Challenges and Advances in Computational Chemistry and Physics, |x 2542-4483 ; |v 41 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/978-3-031-78724-9 |
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| 950 | |a Chemistry and Materials Science (SpringerNature-11644) | ||
| 950 | |a Chemistry and Material Science (R0) (SpringerNature-43709) | ||
| 950 | |a Focus Bundle Chemistry and Materials Science (SpringerNature-85419) | ||