Materials Informatics III : Polymers, Solvents and Energetic Materials / edited by Kunal Roy, Arkaprava Banerjee.

Format:

Book

Author/Creator:

Roy, Kunal.

Contributor:

Banerjee, Arkaprava.

Series:

Challenges and Advances in Computational Chemistry and Physics, 2542-4483 ; 41

Language:

English

Subjects (All):

Cheminformatics.

Machine learning.

Materials.

Catalysis.

Force and energy.

Materials science--Data processing.

Materials science.

Nanochemistry.

Machine Learning.

Materials for Energy and Catalysis.

Computational Materials Science.

Local Subjects:

Cheminformatics.

Machine Learning.

Materials for Energy and Catalysis.

Computational Materials Science.

Nanochemistry.

Physical Description:

1 online resource (550 pages)

Edition:

1st ed. 2025.

Place of Publication:

Cham : Springer Nature Switzerland : Imprint: Springer, 2025.

Summary:

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.

Notes:

Description based on publisher supplied metadata and other sources.

Other Format:

Print version: Roy, Kunal Materials Informatics III

ISBN:

9783031787249

OCLC:

1505735485