Disclaimer, I am more referencing classical molecular dynamics in the atomic region. And what I am envisioning this type of thing for is not dependent on the mathematical model per se. So specifically my point is towards say GROMACS (or a similar MD simulation engine) where the force fields generally used for that are parameterized for biological/organic systems. Let's say we train our neural network on data fitting to lipids. So maybe a bunch of random data on lipids like their melting temperature, surface temperature, etc. Then we run the neural network simulator to model how these things form into larger structures (something that is for the most part impossible with current all atom approaches). And then we study that data to make an assessment on how certain structures can form etc.
Now if you are aware of the field, what I just described was (ignoring I do not remember the exact fitting data) the parameterization process of the MARTINI force field which is sufficiently good at lipids, kind of okay at proteins, and pretty bad at everything else. But within the bounds that you know the weakness of the system you can still use it to figure out experimental data. (Also as an aside MARTINI only got access to proteins and other things later on, thankfully force fields improve over time.)
Now if you are aware of the field, what I just described was (ignoring I do not remember the exact fitting data) the parameterization process of the MARTINI force field which is sufficiently good at lipids, kind of okay at proteins, and pretty bad at everything else. But within the bounds that you know the weakness of the system you can still use it to figure out experimental data. (Also as an aside MARTINI only got access to proteins and other things later on, thankfully force fields improve over time.)