Ab Initio Many Body Quantum Embedding and Local Correlation in Crystalline Materials using Interpolative Separable Density Fitting
Junjie Yang, Ning Zhang, Shunyue Yuan, Jincheng Yu, Hong-Zhou Ye, Garnet Chan

TL;DR
This paper introduces an efficient linear-scaling method for ab initio many-body quantum embedding in periodic systems, enabling accurate ground-state energy calculations for solids using a large number of k-points.
Contribution
It develops a translational symmetry adapted interpolative separable density fitting approach that significantly reduces computational scaling in periodic quantum many-body calculations.
Findings
Achieved linear scaling with the number of k-points.
Successfully computed ground-state energies for weakly and strongly correlated solids.
Provided thermodynamic limit estimates for coupled cluster energies.
Abstract
We present an efficient implementation of ab initio many-body quantum embedding and local correlation methods for infinite periodic systems through translational symmetry adapted interpolative separable density fitting, an approach which reduces the scaling of the calculations to only linear with the number of k-points. Employing this methodology, we compute correlated ground-state coupled cluster energies within density matrix embedding and local natural orbital correlation frameworks for both weakly and strongly correlated solids, using up to 1000 k-points. By extrapolating the local correlation domains and k-point sampling we further obtain estimates of the full coupled cluster with singles, doubles, and perturbative triples ground-state energies in the thermodynamic limit.
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Taxonomy
TopicsMachine Learning in Materials Science · Advanced Chemical Physics Studies · Physics of Superconductivity and Magnetism
