I would like to use an AI-derived force field as they seemed to have lesser computational cost. Which simulation package (Gromacs, Amber, NAMD...) currently implement some of them?
I don't know the implementation details, and if can be counted as "AI derived force field", but, If you're interested in metal complexes, there's a free software, Python-based tool called molSimplify, that uses machine learning to optimize geometry of metal complexes. From their site:
Geometry optimization with density functional theory (DFT), a general procedure to obtain the ground state structures of a complex, is computationally demanding in terms of time and can also easily fail. Two main failure modes are 1) the expected geometry cannot maintain stable during the DFT simulation (e.g., ligand dissociation) and 2) the electronic structure of the optimized geometry is bad, which indicates the system of study is out of the domain of applicability of DFT. Either case can only be identified after a simulation completes, leading to a massive waste of the computational resources (and your time!).
To address this challenge, we built machine learning models to classify the simulation outcomes and readily achieved a good performance on the out-of-sample test data.
For more details, see their paper Duan, Chenru, et al. “Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning Models”. Journal of Chemical Theory and Computation, vol. 15, no 4, abril de 2019, p. 2331–45. ACS Publications, doi:10.1021/acs.jctc.9b00057.