Large-Scale Cubic-Scaling Random Phase Approximation Correlation Energy Calculations Using a Gaussian Basis

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• 作者：Jan Wilhelm ; Patrick Seewald ; Mauro Del Ben ; Jürg Hutter
• 刊名：Journal of Chemical Theory and Computation
• 出版年：2016
• 出版时间：December 13, 2016
• 年：2016
• 卷：12
• 期：12
• 页码：5851-5859
• 全文大小：635K
• ISSN：1549-9626

文摘

We present an algorithm for computing the correlation energy in the random phase approximation (RPA) in a Gaussian basis requiring operations and 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.