论文标题
“嘈杂”力的有效兰格文动力学
Efficient Langevin dynamics for "noisy" forces
论文作者
论文摘要
由于具有系统尺寸的电子结构方法的陡峭缩放，使用第一原理方法的高效玻尔兹曼s采样对扩展系统具有挑战性。随机方法以引入“嘈杂”力的成本提供了更温和的系统大小依赖性，这些力量限制了采样的效率。在一阶Langevin Dynamics（fold）中，可以通过将精心挑选的预处理矩阵与时间步长偏置的降低传播器相结合（Mazzola等人，Phys。Thys。Rev。Lett。，118，115703（2017））来实现有效的采样。但是，当力是嘈杂的时，将S设置为等于力协方差矩阵，该过程严重限制了采样的效率和稳定性。在这里，我们在嘈杂的力下开发了一种新的，一般，最佳和稳定的采样方法。我们将其应用于用随机密度功能理论处理的硅纳米晶体，并通过缩写顺序显示效率提高。
Efficient Boltzmann-sampling using first-principles methods is challenging for extended systems due to the steep scaling of electronic structure methods with the system size. Stochastic approaches provide a gentler system-size dependency at the cost of introducing "noisy" forces, which serve to limit the efficiency of the sampling. In the first-order Langevin dynamics (FOLD), efficient sampling is achievable by combining a well-chosen preconditioning matrix S with a time-step-bias-mitigating propagator (Mazzola et al., Phys. Rev. Lett., 118, 015703 (2017)). However, when forces are noisy, S is set equal to the force-covariance matrix, a procedure which severely limits the efficiency and the stability of the sampling. Here, we develop a new, general, optimal, and stable sampling approach for FOLD under noisy forces. We apply it for silicon nanocrystals treated with stochastic density functional theory and show efficiency improvements by an order-of-magnitude.
