To add some further information to Raz's answer, it is also possible to talk about the nanoparticle case. With computing power getting stronger and codes becoming more efficient it is becoming more and more possible to explore almost physical systems. Nanoparticles for example can be modeled directly based on either experimental observations (TEM, XRD, XPS results) or based on surface energies and wulff constructions.
For these calculations one might go about the following process to create the nanoparticle model.
- Calculate surface energies of all relevant surface terminations, possibly even modeling changes due to coverage effects
- Perform a wulff construction to get a good initial guess at the structure
- Explore deviations in the wulff construction, edges and corners are not modeled directly and their effect becomes stronger and stronger as the particle gets smaller. DFT will tend to not explore massive particles, so this might be a big factor.
It may also be possible to use some type of mixed model for large nanoparticles where part of the nanoparticle is calculated at the DFT theory level and at some distance the DFT is replaced by a force field. This allows for the modeling of corner sites for example which would not be reasonable to model using a pure DFT model.
It is also worthwhile to remember that while solvation is typically needed for calculations in aqueous environments (with a large impact on the surface), implicit models can perform well in non-hydrogen bonding solvation environments. Optionally, you can just restrict yourself to collaborating with people who only work in a perfect vacuum.