Can I read General Quantitative Structure–property Relationships and Machine Learning Correlations to Energetic Material Sensitivities on EtoBox?

General Quantitative Structure–property Relationships and Machine Learning Correlations to Energetic Material Sensitivities by Jason A. Morrill; Brian C. Barnes; Betsy M. Rice; Edward F.C. Byrd is a book available to read on EtoBox.

What is General Quantitative Structure–property Relationships and Machine Learning Correlations to Energetic Material Sensitivities about?

Molecular Modeling of the Sensitivities of Energetic Materials, Volume 22 introduces experimental aspects, explores the relationships between sensitivity, molecular structure and crystal structure, discusses insights from numerical simulations, and highlights applications of these approaches to the design of new materials. Providing practical guidelines for implementing predictive models and their application to the search for new compounds, this book is an authoritative guide to an exciting field of research that warrants a computer-aided approach for the investigation and design of safe and powerful explosives or propellants. Much recent effort has been put into modeling sensitivities, with most work focusing on impact sensitivity and leading to a lot of experimental data in this area. Models must therefore be developed to allow evaluation of significant properties from the structure of constitutive molecules. Highlights a range of approaches for computational simulation and the importance of combining them to accurately understand or estimate different parameters Provides an overview of experimental findings and knowledge in a quick and accessible format Presents guidelines to i

Author

Jason A. Morrill; Brian C. Barnes; Betsy M. Rice; Edward F.C. Byrd

Publisher

Elsevier

Published

2022

Language

EN

ISBN

9780128231104

Subjects

Science, Chemistry, Engineering