One goal of simulations is to predict chemical properties such that experiments can be minimized. A critical part of this is the reliable generation of molecular models. Consequently, we have developed several modular program packages that allow to parameterize entire force fields semi- and fully automatically. Various interlinked modeling resolutions are addressed. For intramolecular interactions, a force-field optimization package named Wolf2Pack [1] has been developed that transfers knowledge gained from quantum mechanics to Newtonian-based molecular models. For intermolecular interactions, especially Lennardâ??Jones parameters, a modular optimization toolkit has been developed that combines global and local optimization algorithms. Global coarse optimization is performed by the meta-model based tool CoSMoS [2], while local finer optimization is done by the gradient-based optimization workflow named GROW [3] or by a derivative-free method called SpaGrOW. The overall goal of our toolkits is to realize an easy, efficient, systematic and user-friendly development of reliable force-field parameters in reasonable time. The various tools are needed and interlinked since different stages of the optimization process demand different courses of action. In this presentation, we will present an overview of these toolkits, our approach towards parameterization (e.g. target observables, transferability, data archiving), and discuss some interesting issues that we have encountered along the way.
 [1] Krämer-Fuhrmann, Ottmar; Neisius, Jens; Gehlen, Niklas; Reith, Dirk; Kirschner, Karl N. (2013): Wolf2Pack--portal based atomistic force-field development. In: J Chem Inf Model 53 (4), S. 802â??808. DOI: 10.1021/ci300290g.
[2] Krämer, Andreas; Hülsmann, Marco; Köddermann, Thorsten; Reith, Dirk (2014): Automated parameterization of intermolecular pair potentials using global optimization techniques. In: Computer Physics Communications 185 (12), S. 3228â??3239. DOI: 10.1016/j.cpc.2014.08.022.
[3] Hülsmann, Marco; Köddermann, Thorsten; Vrabec, Jadran; Reith, Dirk (2010): GROW: A Gradient-based Optimization Workflow for the Automated Development of Molecular Models. In: Computer Physics Communications 181, S. 499â??513. DOI: 10.1021/ci300290g.
