High-pressure and temperature neural network reactive force field for energetic materials
Abstract
Reactive force fields for molecular dynamics have enabled a wide range of studies in numerous material classes. These force fields are computationally inexpensive compared with electronic structure calculations and allow for simulations of millions of atoms. However, the accuracy of traditional force fields is limited by their functional forms, preventing continual refinement and improvement. Therefore, we develop a neural network-based reactive interatomic potential for the prediction of the mechanical, thermal, and chemical responses of energetic materials at extreme conditions. The training set is expanded in an automatic iterative approach and consists of various CHNO materials and their reactions under ambient and shock-loading conditions. This new potential shows improved accuracy over the current state-of-the-art force fields for a wide range of properties such as detonation performance, decomposition product formation, and vibrational spectra under ambient and shock-loading conditions.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Apr 13, 2023
- Source ID
- 10.1063/5.0146055
Entities
People
- Alejandro Strachan
- Brenden W Hamilton
- Md Mahbubul Islam
- Michael N Sakano
- Pilsun Yoo
Organizations
- Los Alamos National Laboratory
- Oak Ridge National Laboratory
- Office of Naval Research
- Purdue University
- Sandia National Laboratories
- Wayne State University