Dittmer Linus Bjarne, Dreuw Andreas
Interdisciplinary Center for Scientific Computing, Ruprecht-Karls University, Im Neuenheimer Feld 205, 69120 Heidelberg, Germany.
J Chem Phys. 2023 Oct 7;159(13). doi: 10.1063/5.0159737.
In this paper we present the Markovian Multiagent Monte-Carlo Second Order Self-Consistent Field Algorithm (M3-SOSCF). This algorithm provides a highly reliable methodology for converging SCF calculations in single-reference methods using a modified differential evolution approach. Additionally, M3 is embarrassingly parallel and modular in regards to Newton-Raphson subroutines. We show that M3 is able to surpass contemporary SOSCFs in reliability, which is illustrated by a benchmark employing poor initial guesses and a second benchmark with SCF calculations which face difficulties using standard SCF algorithms. Furthermore, we analyse inherent properties of M3 and show that in addition to its robustness and efficiency, it is more user-friendly than current SOSCFs.
在本文中,我们提出了马尔可夫多智能体蒙特卡罗二阶自洽场算法(M3-SOSCF)。该算法提供了一种高度可靠的方法,用于使用改进的差分进化方法在单参考方法中收敛自洽场计算。此外,M3在牛顿-拉夫逊子例程方面具有易于并行化和模块化的特点。我们表明,M3在可靠性方面能够超越当代的二阶自洽场方法,这通过使用较差初始猜测的基准测试以及使用标准自洽场算法面临困难的自洽场计算的第二个基准测试得到了说明。此外,我们分析了M3的固有特性,并表明除了其稳健性和效率之外,它比当前的二阶自洽场方法更便于用户使用。