LCT, UMR 7616 CNRS, Sorbonne Université, Paris 75052, France.
IP2CT, FR 2622 CNRS, Sorbonne Université, Paris 75005, France.
J Chem Theory Comput. 2022 Mar 8;18(3):1633-1645. doi: 10.1021/acs.jctc.1c01291. Epub 2022 Feb 8.
We propose a new strategy to solve the key equations of the many-body dispersion (MBD) model by Tkatchenko, DiStasio Jr., and Ambrosetti. Our approach overcomes the original computational complexity that limits its applicability to large molecular systems within the context of density functional theory. First, to generate the required frequency-dependent screened polarizabilities, we introduce an efficient solution to the Dyson-like self-consistent screening equations. The scheme reduces the number of variables and, coupled to a direct inversion of the iterative subspace extrapolation, exhibits linear-scaling performances. Second, we apply a stochastic Lanczos trace estimator resolution to the equations evaluating the many-body interaction energy of coupled quantum harmonic oscillators. While scaling linearly, it also enables communication-free pleasingly parallel implementations. As the resulting stochastic massively parallel MBD approach is found to exhibit minimal memory requirements, it opens up the possibility of computing accurate many-body van der Waals interactions of millions-atoms' complex materials and solvated biosystems with computational times in the range of minutes.
我们提出了一种新策略，通过 Tkatchenko、DiStasio Jr. 和 Ambrosetti 的多体色散 (MBD) 模型来解决关键方程。我们的方法克服了原始计算复杂性，限制了其在密度泛函理论框架内应用于大型分子系统的适用性。首先，为了生成所需的频率相关屏蔽极化率，我们引入了一种有效解决方案，用于处理类似 Dyson 的自洽屏蔽方程。该方案减少了变量的数量，并与迭代子空间外推的直接反转相结合，具有线性比例性能。其次，我们应用随机 Lanczos 迹估计分辨率来解决耦合量子谐振子的多体相互作用能量方程。虽然它呈线性扩展，但也可以实现无通信的愉快并行实现。由于所得到的随机大规模并行 MBD 方法被发现具有最小的内存需求，因此它为计算具有数百万原子的复杂材料和溶剂化生物系统的精确多体范德华相互作用提供了可能性，计算时间在几分钟范围内。
