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 alter...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 hightemperature decomposition mechanisms,challenging the conventional view of material-specific behavior.EMFF-2025 offers a versatile computational framework for accelerating HEM design and optimization.展开更多
基金supported by the National Natural Science Foundation ofChina(Grant 52106130)the State Key Laboratory of Explosion Science and Safety Protection(Grants QNKT23-15).
文摘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 hightemperature decomposition mechanisms,challenging the conventional view of material-specific behavior.EMFF-2025 offers a versatile computational framework for accelerating HEM design and optimization.
