高效GPU加速的神经进化势分析梯度训练
Efficient GPU-Accelerated Training of a Neuroevolution Potential with Analytical Gradients
AI总结：
本文提出基于分析梯度的GNEP训练框架，通过Adam优化器提升训练效率和收敛速度，验证其在Sb-Te材料系统中显著降低拟合时间，保持高精度和物理可解释性，适用于大规模分子动力学模拟。
摘要：
机器学习 interatomic 潜在能（MLIPs）如神经进化势（NEP）结合量子力学准确性与计算效率显著加速原子动态模拟。通过无导数优化训练的常规NEP具有良好精度，但因高维参数搜索效率低。为解决此问题，本文提出采用显式分析梯度和Adam优化器的梯度优化NEP（GNEP）训练框架。该方法显著提升训练效率和收敛速度，同时保持精度和物理可解释性。将GNEP应用于Sb-Te材料系统（数据集包括晶体、液体和无序相）的训练，拟合时间相比NEP训练框架大幅降低，通常降低数个数量级。拟合的势能通过DFT参考计算验证，证明在方程状态和径向分布函数上具有满意的一致性。这些结果证实GNEP在保持高预测精度和可转移性的同时，显著提升了计算效率，使其适用于大规模分子动力学模拟。
英文摘要：
Machine-learning interatomic potentials (MLIPs) such as neuroevolution potentials (NEP) combine quantum-mechanical accuracy with computational efficiency significantly accelerate atomistic dynamic simulations. Trained by derivative-free optimization, the normal NEP achieves good accuracy, but suffers from inefficiency due to the high-dimensional parameter search. To overcome this problem, we present a gradient-optimized NEP (GNEP) training framework employing explicit analytical gradients and the Adam optimizer. This approach greatly improves training efficiency and convergence speedily while maintaining accuracy and physical interpretability. By applying GNEP to the training of Sb-Te material systems(datasets include crystalline, liquid, and disordered phases), the fitting time has been substantially reduced-often by orders of magnitude-compared to the NEP training framework. The fitted potentials are validated by DFT reference calculations, demonstrating satisfactory agreement in equation of state and radial distribution functions. These results confirm that GNEP retains high predictive accuracy and transferability while considerably improved computational efficiency, making it well-suited for large-scale molecular dynamics simulations.
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