Full 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.
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.
