AccelNet：通过笛卡尔矩分解实现多项式角度描述符的精确后向兼容加速
AccelNet: Exact backward-compatible acceleration of polynomial angular descriptors through Cartesian moment factorization
- The University of Tokyo（东京大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究提出AccelNet方法，可无需重训练加速aenet、n2p2神经网络势，通过笛卡尔矩分解替换邻居对循环，重现描述符等至浮点误差，经H₂O、TiO₂模型验证并在LAMMPS中测试，已开源。
AI中文摘要：
我们提出了AccelNet，一种无需重新训练即可加速现有已训练aenet和n2p2神经网络势的精确、后向兼容方法。对于具有可分离单邻居权重且对cosθ有有限多项式依赖的角度项，该方法利用其隐藏的有限秩结构，用单邻居笛卡尔矩替代显式邻居对循环。AccelNet可读取用任一软件包训练的模型，并将其描述符、能量和解析力重现至浮点舍误差水平。我们针对H₂O和TiO₂模型验证了该等价性，并在LAMMPS分子动力学模拟中测试了所得势。该实现、模型转换工具及LAMMPS接口作为开源软件发布。
英文摘要：
We present AccelNet, an exact, backward-compatible method for accelerating existing trained aenet and n2p2 neural-network potentials without retraining. For angular terms with separable one-neighbor weights and a finite polynomial dependence on $\cos θ$, the method exploits their hidden finite-rank structure to replace explicit neighbor-pair loops by one-neighbor Cartesian moments. AccelNet reads models trained with either package and reproduces their descriptors, energies, and analytic forces to floating-point roundoff. We verified this equivalence for H$_2$O and TiO$_2$ models and tested the resulting potentials in LAMMPS molecular-dynamics simulations. The implementation, model-conversion tools, and LAMMPS interfaces are released as open-source software. AccelNet also enables GPU-accelerated molecular dynamics with existing aenet and n2p2 potentials. Moment evaluation achieves speedups of up to 10.9 on a CPU and 15.8 on a GPU relative to direct evaluation within AccelNet. The implementation, model-conversion tools, and LAMMPS interfaces are released as open-source software.
