Challenges and Advances in Computational Chemistry and Physics: Polymers, Solvents and Energetic Materials

Roy, Kunal; Banerjee, Arkaprava

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.

Details

Published by: Springer

Publication Date: 2025-03-02

Format: Hardcover

ISBN-13: 9783031787232

DOI: 10.1007/978-3-031-78724-9

Dimensions: 235cm x155cm

Pages: 371