论文标题
将机器学习和经验力场与瘫痪算法相结合：应用原子缺陷的扩散
Combining machine-learned and empirical force fields with the parareal algorithm: application to the diffusion of atomistic defects
论文作者
论文摘要
在分子动力学的背景下，我们在数值上研究了瘫痪算法的自适应版本。这种自适应变体最初是在[F.中引入的。 Legoll，T。Lelievre和U. Sharma，SISC 2022]。我们将重点放在物理兴趣的测试用例上，其中系统的动力学是由langevin方程建模的，并使用分子动力学软件板模拟。在这项工作中，瘫痪算法将机器学习光谱邻居分析势（SNAP）作为细，参考，电位和嵌入式原子方法电位（EAM）作为粗势。我们考虑在钨晶格中的一个自相关原子，并计算在可稳态状态下系统的平均停留时间。我们的数值结果表明，与Langevin动力学的顺序整合相比，使用自适应瘫痪算法的计算增长显着。我们还确定了一个庞大的数值参数制度，该参数可以达到统计准确性，而不会导致轨迹精度的结果。
We numerically investigate an adaptive version of the parareal algorithm in the context of molecular dynamics. This adaptive variant has been originally introduced in [F. Legoll, T. Lelievre and U. Sharma, SISC 2022]. We focus here on test cases of physical interest where the dynamics of the system is modelled by the Langevin equation and is simulated using the molecular dynamics software LAMMPS. In this work, the parareal algorithm uses a family of machine-learning spectral neighbor analysis potentials (SNAP) as fine, reference, potentials and embedded-atom method potentials (EAM) as coarse potentials. We consider a self-interstitial atom in a tungsten lattice and compute the average residence time of the system in metastable states. Our numerical results demonstrate significant computational gains using the adaptive parareal algorithm in comparison to a sequential integration of the Langevin dynamics. We also identify a large regime of numerical parameters for which statistical accuracy is reached without being a consequence of trajectorial accuracy.
