TY - JOUR
T1 - EMFF-2025
T2 - a general neural network potential for energetic materials with C, H, N, and O elements
AU - Wen, Mingjie
AU - Han, Jiahe
AU - Li, Wenjuan
AU - Chang, Xiaoya
AU - Chu, Qingzhao
AU - Chen, Dongping
N1 - Publisher Copyright:
© The Author(s) 2025.
PY - 2025/12
Y1 - 2025/12
N2 - The discovery and optimization of high-energy materials (HEMs) face challenges due to the computational expense and slow iteration of traditional methods. Neural network potentials (NNPs) have emerged as an efficient alternative to first-principles simulations. This study presents EMFF-2025, a general NNP model for C, H, N, and O-based HEMs, leveraging transfer learning with minimal data from DFT calculations. The model achieves DFT-level accuracy, predicting the structure, mechanical properties, and decomposition characteristics of 20 HEMs. Integrating EMFF-2025 with PCA and correlation heatmaps, we map the chemical space and structural evolution of these HEMs across temperatures. Surprisingly, EMFF-2025 uncovers that most HEMs follow similar high-temperature decomposition mechanisms, challenging the conventional view of material-specific behavior. EMFF-2025 offers a versatile computational framework for accelerating HEM design and optimization. (Figure presented.)
AB - The discovery and optimization of high-energy materials (HEMs) face challenges due to the computational expense and slow iteration of traditional methods. Neural network potentials (NNPs) have emerged as an efficient alternative to first-principles simulations. This study presents EMFF-2025, a general NNP model for C, H, N, and O-based HEMs, leveraging transfer learning with minimal data from DFT calculations. The model achieves DFT-level accuracy, predicting the structure, mechanical properties, and decomposition characteristics of 20 HEMs. Integrating EMFF-2025 with PCA and correlation heatmaps, we map the chemical space and structural evolution of these HEMs across temperatures. Surprisingly, EMFF-2025 uncovers that most HEMs follow similar high-temperature decomposition mechanisms, challenging the conventional view of material-specific behavior. EMFF-2025 offers a versatile computational framework for accelerating HEM design and optimization. (Figure presented.)
UR - https://www.scopus.com/pages/publications/105022111340
U2 - 10.1038/s41524-025-01809-w
DO - 10.1038/s41524-025-01809-w
M3 - Article
AN - SCOPUS:105022111340
SN - 2057-3960
VL - 11
JO - npj Computational Materials
JF - npj Computational Materials
IS - 1
M1 - 333
ER -