Using "soft" constraints / bias in geometry optimization?

Question

I study molecules weakly bound to nanoclusters and surfaces. During geometry optimization, I often run into problems like for example a small metal cluster behave nicely and has a given, stable geometry for 90% of the calculations, then in the remaining 10% of cases, it reorganizes to some other geometry. On one hand, these problems are expected as there are several different stability points around, often with relatively low transition barrier, on the other hand, it is rather annoying when changing a functional or basis set, or something minor chemical change induces such a transformation and makes impossible any comparison between geometries. One can use a geometry constrain to fix the geometry every time, but I feel kind of uneasy about it.

I was wondering if there is an easy way to introduce an extra force, similarly like biases can be introduced in MD simulations to do meta-dynamics, so it smoothly lowers the energy of a given geometry and ideally makes it stable. Technicalities: I am using molecular codes (Gaussian, ORCA) for such calculations at the DFT level.

Answer 1

If you are adamant on adding forces during geometry optimization, you could borrow formalisms from mechanochemistry. We have implemented such formalism using Python (see OpenMechanochem) and it is designed to work with Atomic Simulation Environment (ASE). This would allow you to patch quantum chemical software (molecular mechanics code too), including Gaussian and ORCA. But personally, I think fixing some important coordinates in your cluster can do the trick.