A Valence-Bond-Based Multiconfigurational Density Functional Theory: The λ-DFVB Method Revisited

A recently developed valence-bond-based multireference density functional theory, named lambda-DFVB, is revisited in this paper. lambda-DFVB remedies the double-counting error of electron correlation by decomposing the electron-electron interactions into the wave function term and density functional term with a variable parameter lambda. The lambda value is defined as a function of the free valence index in our previous scheme, denoted as lambda-DFVB(K) in this paper. Here we revisit the lambda-DFVB method and present a new scheme based on natural orbital occupation numbers (NOONs) for parameter lambda, named lambda-DFVB(IS), to simplify the process of lambda-DFVB calculation. In lambda-DFVB(IS), the parameter lambda is defined as a function of NOONs, which are straightforwardly determined from the many-electron wave function of the molecule. Furthermore, lambda-DFVB(IS) does not involve further self-consistent field calculation after performing the valence bond self-consistent field (VBSCF) calculation, and thus, the computational effort in lambda-DFVB(IS) is approximately the same as the VBSCF method, greatly reduced from lambda-DFVB(K). The performance of lambda-DFVB(IS) was investigated on a broader range of molecular properties, including equilibrium bond lengths and dissociation energies, atomization energies, atomic excitation energies, and chemical reaction barriers. The computational results show that lambda-DFVB(IS) is more robust without losing accuracy and comparable in accuracy to high-level multireference wave function methods, such as CASPT2.

Automated summary

The paper revisits lambda-DFVB method and introduces lambda-DFVB(IS) scheme, which simplifies the calculation process by using NOONs as a function of parameter lambda. Lambda-DFVB(IS) demonstrates higher computational efficiency and robustness compared to lambda-DFVB(K), while maintaining accuracy comparable to high-level multireference wave function methods like CASPT2.