论文标题
加速使用数据同化的玻璃材料的模拟退火
Accelerating Simulated Annealing of Glassy Materials with Data Assimilation
论文作者
论文摘要
玻璃过渡的超长松弛时间使得难以通过常规方法构建无定形材料的原子模型。我们提出了一种使用数据同化方法来构建此类原子模型的新方法，该方法通过通过实验数据的惩罚来模拟了准确计算的原子间潜能增强的退火。该方法的优点是，它不仅可以将实验数据重现为诸如反向蒙特卡洛之类的结构改进方法，而且还可以从原子质势能方面获得合理的结构。此外，由于原子间潜力，我们不需要高$ Q $范围衍射数据，这是考虑到短期订单所必需的。持续的同源性分析表明，在中间范围内，通过新方法获得的无定形冰实际上更为有序。
The ultra-long relaxation time of glass transition makes it difficult to construct atomic models of amorphous materials by conventional methods. We propose a novel method for building such atomic models using data assimilation method by simulated annealing with an accurately computed interatomic potential augmented by penalty from experimental data. The advantage of this method is that not only can it reproduce experimental data as the structure refinement methods like reverse Monte Carlo but also obtain the reasonable structure in terms of interatomic potential energy. In addition, thanks to the interatomic potential, we do not need high $Q$ range diffraction data, which is necessary to take into account the short-range order. Persistent homology analysis shows that the amorphous ice obtained by the new method is indeed more ordered at intermediate range.
