Systematically Improvable Tensor Hypercontraction: Interpolative Separable Density-Fitting for Molecules Applied to Exact Exchange, Second- and Third-Order Møller–Plesset Perturbation Theory

• Published in: Journal of chemical theory and computation 2020-01, Vol.16 (1), p.243-263
• Main Authors: Lee, Joonho, Lin, Lin, Head-Gordon, Martin
• Format: Article
• Language: English
• Subjects: Algorithms; Atomizing; Basis functions; Density; Exchanging; Factorization; Interpolation; Perturbation theory; Quadratures; Tensors; Thermochemistry; Wave functions
• ISSN: 1549-9618; 1549-9626
• DOI: 10.1021/acs.jctc.9b00820

We present a systematically improvable tensor hypercontraction (THC) factorization based on interpolative separable density fitting (ISDF). We illustrate algorithmic details to achieve this within the framework of Becke’s atom-centered quadrature grid. A single ISDF parameter c ISDF controls the trade-off between accuracy and cost. In particular, c ISDF sets the number of interpolation points used in THC, N IP = c ISDF × N X with N X being the number of auxiliary basis functions. In conjunction with the resolution-of-the-identity (RI) technique, we develop and investigate the THC-RI algorithms for cubic-scaling exact exchange for Hartree–Fock and range-separated hybrids (e.g., ωB97X-V) and quartic-scaling second- and third-order Møller–Plesset theory (MP2 and MP3). These algorithms were evaluated over the W4-11 thermochemistry (atomization energy) set and A24 noncovalent interaction benchmark set with standard Dunning basis sets (cc-pVDZ, cc-pVTZ, aug-cc-pVDZ, and aug-cc-pVTZ). We demonstrate the convergence of THC-RI algorithms to numerically exact RI results using ISDF points. Based on these, we make recommendations on c ISDF for each basis set and method. We also demonstrate the utility of THC-RI exact exchange and MP2 for larger systems such as water clusters and C20. We stress that more challenges await in obtaining accurate and numerically stable THC factorization for wave function amplitudes as well as for the space spanned by virtual orbitals in large basis sets and implementing sparsity-aware THC-RI algorithms.