Department of Chemistry, Columbia University, New York, New York 10027, United States.
Courant Institute of Mathematical Sciences, New York University, New York, New York 10012, United States.
J Chem Theory Comput. 2020 Sep 8;16(9):5572-5585. doi: 10.1021/acs.jctc.0c00437. Epub 2020 Aug 11.
We present three modifications to our recently introduced fast randomized iteration method for full configuration interaction (FCI-FRI) and investigate their effects on the method's performance for Ne, HO, and N. The initiator approximation, originally developed for full configuration interaction quantum Monte Carlo, significantly reduces statistical error in FCI-FRI when few samples are used in compression operations, enabling its application to larger chemical systems. The semistochastic extension, which involves exactly preserving a fixed subset of elements in each compression, improves statistical efficiency in some cases but reduces it in others. We also developed a new approach to sampling excitations that yields consistent improvements in statistical efficiency and reductions in computational cost. We discuss possible strategies based on our findings for improving the performance of stochastic quantum chemistry methods more generally.
我们提出了对我们最近引入的全组态相互作用快速随机迭代方法(FCI-FRI)的三种修改,并研究了它们对该方法在 Ne、HO 和 N 上的性能的影响。启动器逼近最初是为全组态相互作用量子蒙特卡罗开发的,当在压缩操作中使用很少的样本时,它可以显著减少 FCI-FRI 中的统计误差,从而使其能够应用于更大的化学系统。半随机扩展,它涉及在每个压缩中精确地保留固定的元素子集,在某些情况下提高了统计效率,而在其他情况下则降低了统计效率。我们还开发了一种新的激发抽样方法,在提高统计效率和降低计算成本方面都取得了一致的改进。我们根据我们的发现讨论了一般提高随机量子化学方法性能的可能策略。