论文标题
原子模拟的机器学习潜力中的收敛加速度
Convergence Acceleration in Machine Learning Potentials for Atomistic Simulations
论文作者
论文摘要
原子模拟的机器学习电位（MLP）对材料建模具有巨大的前瞻性影响，提供了密度函数理论（DFT）计算的数量级速度，而无需在预测材料特性的预测中牺牲准确性。但是，培训MLP所需的大型数据集的产生令人生畏。在此，我们表明，基于MLP的材料属性预测相对于Brillouin区域集成的精度比基于DFT的属性预测更快。我们证明，这种现象在不同金属系统的材料特性之间是可靠的。此外，我们提供统计误差指标，以准确确定MLP的DFT训练数据集所需的精度水平，以确保材料属性预测的加速收敛，从而大大降低了MLP开发的计算费用。
Machine learning potentials (MLPs) for atomistic simulations have an enormous prospective impact on materials modeling, offering orders of magnitude speedup over density functional theory (DFT) calculations without appreciably sacrificing accuracy in the prediction of material properties. However, the generation of large datasets needed for training MLPs is daunting. Herein, we show that MLP-based material property predictions converge faster with respect to precision for Brillouin zone integrations than DFT-based property predictions. We demonstrate that this phenomenon is robust across material properties for different metallic systems. Further, we provide statistical error metrics to accurately determine a priori the precision level required of DFT training datasets for MLPs to ensure accelerated convergence of material property predictions, thus significantly reducing the computational expense of MLP development.
