Coordinate Descent Full Configuration Interaction.

@article{Wang2019CoordinateDF,
  title={Coordinate Descent Full Configuration Interaction.},
  author={Zhe Wang and Yingzhou Li and Jianfeng Lu},
  journal={Journal of chemical theory and computation},
  year={2019},
  volume={15 6},
  pages={
          3558-3569
        }
}
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… 

Approaching the full configuration interaction ground state from an arbitrary wavefunction with gradient descent and quasi-Newton algorithms.

This work considers gradient descent and quasi-Newton algorithms to optimize the full configuration interaction (FCI) ground state wavefunction starting from an arbitrary reference state |0⟩ and shows an application of the algorithm with reference wavefunctions constructed as linear combinations of non-orthogonal determinants.

Optimal Orbital Selection for Full Configuration Interaction (OptOrbFCI): Pursuing Basis Set Limit under Budget.

An optimal orbital selection for FCI (OptOrbFCI) is proposed to boost the power of existing FCI solvers to pursue the basis set limit under computational budget and the efficiency and accuracy of the method is demonstrated.

Efficient and stochastic multireference perturbation theory for large active spaces within a full configuration interaction quantum Monte Carlo framework.

It is found that while complete active space second-order perturbation theory seems less amenable to a stochastic reformulation, strongly contracted N-Electron Valence second- order Perturbation Theory (NEVPT2) is far more stable, requiring a similar number of walkers to converge the sc-NEVPt2 expectation values as to convergeThe underlying CI problem.

Improved Fast Randomized Iteration Approach to Full Configuration Interaction.

Three modifications to the recently introduced fast randomized iteration method for full configuration interaction (FCI-FRI) are presented and a new approach to sampling excitations is developed that yields consistent improvements in statistical efficiency and reductions in computational cost.

Generalized Many-Body Expanded Full Configuration Interaction Theory.

It is argued that generalized MBE-FCI theory possesses an immense potential to yield near-exact correlation energies for molecular systems of unprecedented size, composition, and complexity in the years to come.

Further Development of iCIPT2 for Strongly Correlated Electrons.

This improved implementation of iCIPT2 can handle 1 order of magnitude more CSFs than the previous version, as revealed by taking the chromium dimer and an iron-sulfur cluster, [Fe2S2(SCH3)]42-, as examples.

Ground and excited state first-order properties in many-body expanded full configuration interaction theory.

The recently proposed many-body expanded full configuration interaction (MBE-FCI) method is extended to excited states and static first-order properties different from total, ground state correlation energies to admit a wide range of future applications by means of many- body expanded treatments of electron correlation.

The Ground State Electronic Energy of Benzene.

We report on the findings of a blind challenge devoted to determining the frozen-core, full configuration interaction (FCI) ground-state energy of the benzene molecule in a standard

Machine learning configuration interaction for ab initio potential energy curves.

  • J. P. Coe
  • Computer Science
    Journal of chemical theory and computation
  • 2019
It is shown that, for all of the considered systems, accurate potential energy curves can now be efficiently computed with MLCI and can achieve lower errors than stochastically selecting configurations while also using substantially less processor hours.

Machine learning configuration interaction for ab initio potential energy curves

It is shown that, for all of the considered systems, accurate potential energy curves can now be efficiently computed with MLCI and can achieve lower errors than stochastically selecting configurations while also using substantially less data.

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