aceEMFF-2025: a general neural network potential for energetic materials with C, H, N, and O elements
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 decompositi…
EMFF-2025 provides DFT-level accuracy for structure, mechanical properties, and decomposition across 20 C, H, N, O energetic materials while enabling faster iteration than first-principles methods.
Evidence
- Peer-reviewednpj Computational Materials2025-11-17
How should this claim be treated?
Truvace Impact Record TRV-2026-0555, v1: “EMFF-2025: a general neural network potential for energetic materials with C, H, N, and O elements.” Truvace, 2026-07-24. /record/TRV-2026-0555 (accessed at citation time). sha256 139078a158f9f3c5…
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