Improving the efficiency of the multireference driven similarity renormalization group via sequential transformation, density fitting, and the non-interacting virtual orbital approximation
Tianyuan Zhang, Chenyang Li, Francesco A. Evangelista

TL;DR
This paper introduces sequential transformation, density fitting, and a virtual orbital approximation to significantly enhance the computational efficiency of the multireference driven similarity renormalization group method, enabling larger system applications.
Contribution
It proposes a new sequential MR-LDSRG(2) approach combined with density fitting and a virtual orbital approximation, reducing computational cost while maintaining accuracy.
Findings
DF-sq-MR-LDSRG(2)+NIVO achieves accuracy comparable to original MR-LDSRG(2) and CCSD.
The method enables calculations on systems with over 550 basis functions.
Automerization energy of cyclobutadiene computed with the new approach matches high-level methods.
Abstract
This study examines several techniques to improve the efficiency of the linearized multireference driven similarity renormalization group truncated to one- and two-body operators [MR-LDSRG(2)]. We propose a sequential MR-LDSRG(2) [sq-MR-LDSRG(2)] approach, in which one-body rotations are folded exactly into the Hamiltonian. This new approach is combined with density fitting (DF) to reduce the storage cost of two-electron integrals. To further avoid the storage of large four-index intermediates, we propose a non-interacting virtual orbit (NIVO) approximation in which tensor blocks labeled by three and four virtual indices are neglected. The NIVO approximation reduces the computational cost prefactor of the MR-LDSRG(2) bringing it closer to that of coupled cluster with singles and doubles (CCSD). We test the effect of the DF and NIVO approximations on the MR-LDSRG(2) and sq-MR-LDSRG(2)…
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Taxonomy
TopicsAdvanced Chemical Physics Studies · Advanced NMR Techniques and Applications · Spectroscopy and Quantum Chemical Studies
