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 as compared to 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 response of energetic materials at extreme conditions for energetic materials. The training set is expanded in an automatic iterative approach and consists of various CHNO materials and their reactions under ambient and under 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.

References in corpus (10)

• Gaussian Approximation Potentials: the accuracy of quantum mechanics, without the electrons
• SchNet - a deep learning architecture for molecules and materials
• E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials
• A Spectral Analysis Method for Automated Generation of Quantum-Accurate Interatomic Potentials
• How van der Waals interactions determine the unique properties of water
• WACSF - Weighted Atom-Centered Symmetry Functions as Descriptors in Machine Learning Potentials
• Applying machine learning techniques to predict the properties of energetic materials
• Multiscale Modeling of Shock Wave Localization in Porous Energetic Material
• Machine Learning Interatomic Potential for Simulations of Carbon at Extreme Conditions
• Energy Localization Efficiency in TATB Pore Collapse Mechanisms

Cited by in corpus (2)

• Data Fusion of Deep Learned Molecular Embeddings for Property Prediction
• Competing Hydrogenation Pathways to Metastable CaH${}_6$ Revealed by Machine-Learning-Potential Molecular Dynamics