The decomposition network of ammonium perchlorate (AP) is essential for combustion
performance and safety of solid propellants, while the detailed reaction pathway during …

Molecular simulations of high energetic materials (HEMs) are limited by efficiency and
accuracy. Recently, neural network potential (NNP) models have achieved molecular …

Al–Li alloys are feasible and promising additives in advanced energy and propellant
systems due to the significantly enhanced heat release and increased specific impulse. The …

The machine learning potential has emerged as a promising approach for addressing the
accuracy-versus-efficiency dilemma in molecular modeling. Efficiently exploring chemical …

Micro-unit composite fuel (Al@ AP) is a promising strategy for achieving stable and efficient
combustion, due to its reduced pressure dependency and ability to prevent agglomeration …

A systematic prediction of crystal structures of the Li x Ge (x= 1–8) system is performed using
a combination of structure prediction and ab initio calculations coupled with deep learning …

From the perspective of a bond-order-length-strength correlation, we put forward an
analytical solution to describe the size, shape, and wall thickness dependency of melting …

A neural network potential (NNP) is developed to investigate the decomposition mechanism
of RDX, AP, and their composites. Utilizing an ab initio dataset, the NNP is evaluated in …

Although the physical properties of CaO have been extensively discussed as a crucial
component of the Earth's mantle, there remains a significant discrepancy between the …