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量子蒙特卡罗中大型行列式展开的优化

Optimization of Large Determinant Expansions in Quantum Monte Carlo.

作者信息

Ammar Abdallah, Giner Emmanuel, Scemama Anthony

机构信息

Laboratoire de Chimie et Physique Quantiques (UMR 5626), Université de Toulouse - CNRS, 118 route de Narbonne, 31062 Toulouse cedex 09, France.

Laboratoire de Chimie Théorique (UMR 7616), Université Paris Sorbonne - CNRS, 4 place Jussieu, 75052 Paris cedex, France.

出版信息

J Chem Theory Comput. 2022 Sep 13;18(9):5325-5336. doi: 10.1021/acs.jctc.2c00556. Epub 2022 Aug 23.

Abstract

We present a new method for the optimization of large configuration interaction (CI) expansions in the quantum Monte Carlo (QMC) framework. The central idea here is to replace the nonorthogonal variational optimization of CI coefficients performed in usual QMC calculations by an orthogonal non-Hermitian optimization thanks to the so-called transcorrelated (TC) framework, the two methods yielding the same results in the limit of a complete basis set. By rewriting the TC equations as an effective self-consistent Hermitian problem, our approach requires the sampling of a single quantity per Slater determinant, leading to minimal memory requirements in the QMC code. Using analytical quantities obtained from both the TC framework and the usual CI-type calculations, we also propose improved estimators which reduce the statistical fluctuations of the sampled quantities by more than an order of magnitude. We demonstrate the efficiency of this method on wave functions containing 10-10 Slater determinants, using effective core potentials or all-electron calculations. In all the cases, a sub-milli-Hartree convergence is reached within only two or three iterations of optimization.

摘要

我们提出了一种在量子蒙特卡罗(QMC)框架中优化大组态相互作用(CI)展开的新方法。这里的核心思想是,借助所谓的转关联(TC)框架,将通常QMC计算中对CI系数进行的非正交变分优化替换为正交非厄米优化,在完备基组极限下这两种方法会得到相同的结果。通过将TC方程重写为一个有效的自洽厄米问题,我们的方法要求对每个斯莱特行列式采样一个单一量,从而使QMC代码中的内存需求最小化。利用从TC框架和通常的CI型计算中获得的解析量,我们还提出了改进的估计量,可将采样量的统计波动降低一个数量级以上。我们使用有效核势或全电子计算,在包含10 - 10个斯莱特行列式的波函数上证明了该方法的效率。在所有情况下,仅通过两到三次优化迭代就能达到亚毫哈特里的收敛。

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