| Author | : Didier Mathieu |
| Publisher | : Elsevier |
| Release Date | : 01 April 2022 |
| ISBN 10 | : 0128231106 |
| Pages | : 486 pages |
| Rating | : /5 ( users) |
The strict safety requirements associated with experimental studies of energetic materials warrant a computer-aided approach for the investigation and design of safe and powerful explosives or propellants. Models must therefore be developed to allow evaluation of significant properties from the structure of constitutive molecules. Much recent effort has been put into modeling sensitivities, with most work focusing on impact sensitivity, leading to a lot of experimental data in this area. Modern machine learning techniques, new physics-based models, and new reactive molecular dynamics and multiscale simulation methods have subsequently led to quantitative procedures applicable to large datasets and yielded valuable insight into the underlying initiation mechanisms. Molecular Modeling of the Sensitivities of Energetic Materials highlights these latest developments. Beginning with an introduction to experimental aspects in Part I, Parts II and III then explore relationships between sensitivity, molecular structure, and crystal structure, before going on to discuss insights from numerical simulations in Part IV. Part V then 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, Molecular Modeling of the Sensitivities of Energetic Materials is an authoritative guide to this exciting field of research. Highlights a range of approaches for computational simulation and the importance of combining these to accurately understand or estimate different parameters Provides an overview of experimental findings and knowledge in a quick, accessible format Presents guidelines to implement sensitivity models using open-source python-related software, supporting easy implementation of flexible models, and allowing fast assessment of hypotheses
