Open Access

Specifically designed and accurate force fields are of central importance in molecular simulations, as they are often required when investigating new or slightly modified systems. Their parameterization, referred to as force-field parameter optimization, is a complex multi-modal optimization challenge. It requires balancing the parameter’s transferability between various optimization objectives, while their interdependencies often are non-trivial and hard to unravel. This cumulative dissertation addresses this optimization challenge by systematically developing and extending an automatized multi-scale force-field parameter optimization workflow. An important feature is ist modular design that (a) allows the optimization of any amount and combination of force-field parameters towards any type and amount of target properties, and (b) facilitates the extension by additional optimization algorithms or objective functions. First, the workflow’s foundation and a proof-of-concept is provided by simultaneously optimizing the Lennard-Jones parameters towards n-octane’s liquid-phase density (i.e. a multi-molecular, thermodynamic property) and its relative conformational Energies (i.e. a single molecular structural property). By showing that the applicability of the force field was expanded, the simultaneous multi-scale optimization workflow is established. Then, the optimization workflow’s hyperparameters are fine-tuned to improveits results and it is shown that by reasonably balancing the optimization objectives using weighting factors the previously introduced errors are reduced. Next, the optimization procedure’s efficiency is increased by substituting the most time-consuming molecular dynamics simulations by machine learning surrogate models. Those simulations are required repeatedly throughout the optimization, and due to the workflow’s iterative nature, they need to be performed just-in-time. By substituting them with machine learning surrogate models the required run time is approximately reduced by 20-fold. Additionally, guidelines for the model’s training and selection are included. Subsequently, as a challenging test of the workflow, the Lennard-Jones Parameters for 1-bromobutane and 2-bromobutane are optimized. They exhibit a σ-hole allowing halogen bonding, which is subject to current research, that can be supported by accurate simulation models. It is shown that the modeling is improved by the herein presented multi-scale optimization approach, but that further refinements with respect to the workflow and the modeling are necessary. In conclusion, promising approaches to improve the optimization workflow and the modeling are suggested.