Wavefunction-based quantum methods are some of the most accurate tools for predicting and analyzing the electronic structure of molecules, in particular for accounting for dynamical electron correlation. However, most methods of including dynamical correlation beyond the simple second-order Møller-Plesset perturbation theory (MP2) level are too computationally expensive to apply to large molecules. Approximations which reduce scaling with system size are a potential remedy, such as the tensor hyper-contraction (THC) technique of Hohenstein et al., but also result in additional sources of error. In this work, we correct errors in THC-approximated methods using machine learning. Specifically, we apply THC to third-order Møller-Plesset theory (MP3) as a simplified model for coupled cluster with single and double excitations (CCSD), and train several regression models on observed THC errors from the Main Group Chemistry Database (MGCDB84). We compare performance of multiple linear regression models and non-linear Kernel Ridge regression models. We also investigate correlation procedures using absolute and relative corrections and evaluate the corrections for both molecule and reaction energies. We discuss the potential for using regression techniques to correct THC-MP3 errors by comparing it to the "canonical" MP3 reference values and find the optimum technique based on accuracy. We find that non-linear regression models reduced root mean squared errors between THC- and canonical MP3 by a factor of 6-9$\times$ for total molecular energies and 2-3$\times$ for reaction energies.
Tensor hypercontraction: A universal technique for the resolution of matrix elements of local, finite-range $N$-body potentials in many-body quantum problems
Enhanced Krylov Methods for Molecular Hamiltonians: Reduced Memory Cost and Complexity Scaling via Tensor Hypercontraction
Technical University of Munich（慕尼黑工业大学）；Max Planck Institute of Quantum Optics（马克斯·普朗克量子光学研究所）；Munich Center for Quantum Science and Technology（慕尼黑量子科学与技术中心）
Tensor hypercontraction for self-consistent vertex corrected GW with static and dynamic screening; applications to molecules and solids with superexchange
Systematically Improvable Tensor Hypercontraction: Interpolative Separable Density-Fitting for Molecules Applied to Exact Exchange, Second- and Third-Order Møller-Plesset Perturbation Theory
提出了一种基于插值可分密度拟合的张量超压缩（THC）分解方法。我们说明算法的细节，以实现这一目标的框架内，贝克的原子为中心的正交网格。单个ISDF参数$c_\text{ISDF}$控制精度和成本之间的权衡。具体地，$c_\text{ISDF}$设置在THC中使用的内插点的数量，$N_\text{IP} = c_\text{ISDF}\times N_\text{X}$，其中$N_\text{X}$是辅助基函数的数量。结合身份解析（RI）技术，我们开发并研究了用于Hartree-Fock和范围分离混合体（例如，$ω$ B 97 X-V）和四次标度二阶和三阶Møller-Plesset理论（MP2和MP3）。这些算法在W 4 -11热化学（原子化能）集和A24非共价相互作用基准集与标准Dunning基组（cc-pVDZ、cc-pVTZ、aug-cc-pVDZ和aug-cc-pVTZ）上进行了评价。我们证明了收敛的THC-RI算法的数值精确RI结果使用ISDF点。在此基础上，我们对每个基组和方法的$c_\text{ISDF}$提出了建议。我们还演示了THC-RI精确交换和MP2对于水团簇和$\text{C}_{20}$等大型系统的实用性。我们强调，更多的挑战等待在获得准确的和数值稳定的THC分解波函数振幅，以及在大基组的虚拟轨道所跨越的空间，并实现稀疏意识的THC-RI算法。
Tensor hypercontraction for fully self-consistent imaginary-time GF2 and GWSOX methods: theory, implementation, and role of the Green's function second-order exchange for intermolecular interactions
