semiempiiriset

Semiempirical methods are a class of computational chemistry techniques used to approximate solutions to the Schrödinger equation. They achieve this by incorporating experimental data or results from more accurate theoretical calculations to simplify the problem. Unlike ab initio methods, which aim to solve the fundamental equations directly with minimal approximations, semiempirical methods introduce empirical parameters derived from fitting to known values. These parameters absorb some of the complexity of electron-electron repulsion and other interactions, making the calculations significantly faster and less computationally demanding.

The core idea behind semiempirical methods is to neglect or approximate certain terms in the theoretical Hamiltonian that are difficult to calculate accurately. For example, some integrals that represent electron-electron interactions might be simplified or replaced by parameters that have been "tuned" to reproduce experimental observations like bond lengths, energies, or spectral data. This empirical adjustment allows semiempirical methods to achieve reasonable accuracy for certain properties with a fraction of the computational cost of more rigorous approaches.

These methods are particularly useful for studying larger molecules where ab initio calculations become prohibitively expensive. They are often employed in areas such as molecular modeling, drug discovery, and materials science to quickly explore conformational spaces, predict reaction pathways, and estimate various molecular properties. While they are not as accurate as high-level ab initio or density functional theory (DFT) methods, their speed and ability to handle larger systems make them valuable tools in computational chemistry research. Examples of common semiempirical methods include AM1, PM3, and PM6.