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Large-Scale Cubic-Scaling Random Phase Approximation Correlation Energy Calculations Using a Gaussian Basis
journal contribution
posted on 2016-10-25, 00:00 authored by Jan Wilhelm, Patrick Seewald, Mauro Del Ben, Jürg HutterWe present an algorithm for computing
the correlation energy in
the random phase approximation (RPA) in a Gaussian basis requiring O(N3) operations and O(N2) memory. The method is based on the resolution
of the identity (RI) with the overlap metric, a reformulation of RI-RPA
in the Gaussian basis, imaginary time, and imaginary frequency integration
techniques, and the use of sparse linear algebra. Additional memory
reduction without extra computations can be achieved by an iterative
scheme that overcomes the memory bottleneck of canonical RPA implementations.
We report a massively parallel implementation that is the key for
the application to large systems. Finally, cubic-scaling RPA is applied
to a thousand water molecules using a correlation-consistent triple-ζ
quality basis.