用神经网络势能模型高效预测含C/H/N/O高能材料性质，精度接近量子计算。
A General Neural Network Potential for Energetic Materials with C, H, N, and O elements
- 基于迁移学习微调预训练模型，快速适配20种高能材料体系。
- 在分子动力学模拟中准确预测结构、力学与分解特性，保持DFT级精度。
- 适合材料研发人员加速高能材料设计，开源模型支持复现与应用。
高能材料（HEMs）的发现与优化受限于传统方法高昂的计算成本和漫长的开发周期。本文提出一种通用神经网络势能模型（NNP），可高效预测含C、H、N、O元素的高能材料的结构、力学及分解性质。该框架利用预训练的NNP模型，通过密度泛函理论（DFT）计算所得的能量与力数据进行迁移学习微调，实现对20种不同HEM体系的快速适应，同时保持接近DFT的精度，显著降低计算成本。关键优势在于模型能有效捕捉高能材料的化学活性空间，精确描述热分解过程中的关键原子相互作用与反应机制。该通用NNP已应用于分子动力学模拟，并与实验数据验证一致。相比传统力场，其在微观与宏观性质上均展现出更优的准确性与泛化能力，大幅降低计算与实验成本。本工作为高能材料的设计开发提供高效策略，并构建了融合DFT、机器学习与实验研究的可扩展框架。模型已在GitHub开源：https://github.com/MingjieWen/General-NNP-model-for-C-H-N-O-Energetic-Materials。
原文摘要 · Abstract (English)
The discovery and optimization of high-energy materials (HEMs) are constrained by the prohibitive computational expense and prolonged development cycles inherent in conventional approaches. In this work, we develop a general neural network potential (NNP) that efficiently predicts the structural, mechanical, and decomposition properties of HEMs composed of C, H, N, and O. Our framework leverages pre-trained NNP models, fine-tuned using transfer learning on energy and force data derived from density functional theory (DFT) calculations. This strategy enables rapid adaptation across 20 different HEM systems while maintaining DFT-level accuracy, significantly reducing computational costs. A key aspect of this work is the ability of NNP model to capture the chemical activity space of HEMs, accurately describe the key atomic interactions and reaction mechanisms during thermal decomposition. The general NNP model has been applied in molecular dynamics (MD) simulations and validated with experimental data for various HEM structures. Results show that the NNP model accurately predicts the structural, mechanical, and decomposition properties of HEMs by effectively describing their chemical activity space. Compared to traditional force fields, it offers superior DFT-level accuracy and generalization across both microscopic and macroscopic properties, reducing the computational and experimental costs. This work provides an efficient strategy for the design and development of HEMs and proposes a promising framework for integrating DFT, machine learning, and experimental methods in materials research. (To facilitate further research and practical applications, we open-source our NNP model on GitHub: https://github.com/MingjieWen/General-NNP-model-for-C-H-N-O-Energetic-Materials.)
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