Gonzales James E, Hwang Wonmuk, Brooks Bernard R
Department of Biomedical Engineering, Texas A&M University, College Station, Texas 77843, USA.
Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, USA.
J Chem Phys. 2025 Jun 14;162(22). doi: 10.1063/5.0264935.
Computing electrostatic interactions remains the bottleneck of molecular dynamics (MD) simulations despite more than a century of effort in developing methods to accelerate the calculation. Previously, we have developed the spherical grids and treecode and Gauss-Legendre-spherical-t (GLST) algorithms for electrostatic interactions. Here, we explain the computational details and discuss the performance of GLST. The GLST algorithm achieves O(N) scaling and should be less demanding in parallel communication compared with the widely used particle mesh Ewald method and likely comparable to the communication costs of the fast multipole method. We find that GLST is suitable for rapid calculation of long-range electrostatic interactions in MD simulations as it has highly tunable accuracy and should scale well on massively parallel computing architectures. The GLST software presented here is available as a standalone library on GitHub.
尽管在开发加速计算方法方面已经付出了一个多世纪的努力,但计算静电相互作用仍然是分子动力学(MD)模拟的瓶颈。此前,我们已经开发了用于静电相互作用的球形网格、树码和高斯 - 勒让德 - 球谐(GLST)算法。在此,我们解释计算细节并讨论GLST的性能。GLST算法实现了O(N)的计算复杂度,与广泛使用的粒子网格埃瓦尔德方法相比,其在并行通信方面的要求应该更低,并且可能与快速多极子方法的通信成本相当。我们发现GLST适用于MD模拟中长程静电相互作用的快速计算,因为它具有高度可调节的精度,并且在大规模并行计算架构上应该具有良好的扩展性。这里展示的GLST软件可作为一个独立库在GitHub上获取。