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10.6 MB
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April 21, 2026, 11:56 a.m.
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(Last updated: April 21, 2026, 11:56 a.m.)
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| ['Roy K. Materials Informatics III. Polymers,Solvents and Energetic Materials 2025.pdf'] | 0 bytes |
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| Uploaded by andryold1 | Size 32.9 MB | Health [ 19 /41 ] | Added 2026-04-20 |
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| Uploaded by andryold1 | Size 11.0 MB | Health [ 39 /18 ] | Added 2026-04-20 |
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NOTE
SOURCE: Roy K. Materials Informatics III. Polymers,Solvents and Energetic Materials 2025
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COVER
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MEDIAINFO
Textbook in PDF format
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.
Introduction
Introduction to Machine Learning for Predictive Modeling II
Introduction to Predicting Properties of Organic Materials
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
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
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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