Machine learning for a finite size correction in periodic coupled cluster theory calculations
arXiv:2204.00092 · doi:10.1063/5.0086580
Abstract
We introduce a straightforward Gaussian process regression (GPR) model for the transition structure factor of metal periodic coupled cluster singles and doubles (CCSD) calculations. This is inspired by the method introduced by Liao and Grüneis for interpolating over the transition structure factor to obtain a finite size correction for CCSD [J. Chem. Phys. 145, 141102 (2016)], and by our own prior work using the transition structure factor to efficiently converge CCSD for metals to the thermodynamic limit [Nat. Comput. Sci. 1, 801 (2021)]. In our CCSD-FS-GPR method to correct for finite size errors, we fit the structure factor to a 1D function in the momentum transfer, . We then integrate over this function by projecting it onto a k-point mesh to obtain comparisons with extrapolated results. Results are shown for lithium, sodium, and the uniform electron gas.
7 pages, 2 figures
References in corpus (9)
- Quantum embedding theories
- The Finite Size Error in Many-body Simulations with long-Ranged Interactions
- Finite-size correction in many-body electronic structure calculations
- Applying the Coupled-Cluster Ansatz to Solids and Surfaces in the Thermodynamic Limit
- Local embedding of Coupled Cluster theory into the Random Phase Approximation using plane-waves
- An optimized twist angle to find the twist-averaged correlation energy applied to the uniform electron gas
- Structural and electronic properties of solid molecular hydrogen from many-electron theories
- Absorption Spectra of Solids from Periodic Equation-of-Motion Coupled-Cluster Theory
- Efficient Gaussian Process Regression for prediction of molecular crystals harmonic free energies