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Coordinate Descent Full Configuration Interaction.

Zhe WangYingzhou LiJianfeng Lu
Published in: Journal of chemical theory and computation (2019)
We develop an efficient algorithm, coordinate descent FCI (CDFCI), for the electronic structure ground-state calculation in the configuration interaction framework. CDFCI solves an unconstrained nonconvex optimization problem, which is a reformulation of the full configuration interaction eigenvalue problem, via an adaptive coordinate descent method with a deterministic compression strategy. CDFCI captures and updates appreciative determinants with different frequencies proportional to their importance. We show that CDFCI produces accurate variational energy for both static and dynamic correlation by benchmarking the binding curve of nitrogen dimer in the cc-pVDZ basis with 10-3 mHa accuracy. We also demonstrate the efficiency and accuracy of CDFCI for strongly correlated chromium dimer in the Ahlrichs VDZ basis and produce state-of-the-art variational energy.
Keyphrases
  • machine learning
  • deep learning
  • high resolution
  • mass spectrometry