Force field (FF) based molecular modeling is an often used method to investigate and study structural and dynamic properties of (bio-)chemical substances and systems. When such a system is modeled or refined, the force field parameters need to be adjusted. This force field parameter optimization can be a tedious task and is always a trade-off in terms of errors regarding the targeted properties. To better control the balance of various properties’ errors, in this study we introduce weighting factors for the optimization objectives. Different weighting strategies are compared to fine-tune the balance between bulk-phase density and relative conformational energies (RCE), using n-octane as a representative system. Additionally, a non-linear projection of the individual property-specific parts of the optimized loss function is deployed to further improve the balance between them. The results show that the overall error is reduced. One interesting outcome is a large variety in the resulting optimized force field parameters (FFParams) and corresponding errors, suggesting that the optimization landscape is multi-modal and very dependent on the weighting factor setup. We conclude that adjusting the weighting factors can be a very important feature to lower the overall error in the FF optimization procedure, giving researchers the possibility to fine-tune their FFs.

Force field-based models are a Newtonian mechanics approximation of reality and are inherently noisy. Coupling models from different molecular scale domains (including single, gas-phase molecules up to multimolecule, condensed phase ensembles) is difficult, which is also the case for finding solutions that transfer well between the scales. In this contribution, we introduce a surrogate-assisted algorithm to optimize Lennard-Jones parameters for target data from different scale domains to overcome the difficulties named above. Specifically, our approach combines a surrogate-assisted global evolutionary optimization method with a presampling phase that takes advantage of one scale domain being less computationally expensive to evaluate. The algorithm's components were evaluated individually, elucidating their individual merits. Our findings show that the process of parametrizing force fields can significantly benefit from both the presampling method, which alleviates the need to have a good initial guess for the parameters, and the surrogate model, which improves efficiency.

This contribution is a proof-of-concept that a diverse set of training observables leads to a meaningful force field even if a very limited number of thermodynamic state points (i.e. temperatures) is used. This approach generates optimized force-field parameters, enabling the user to extract additional information from MD simulations. The ultimate goal is to extend this approach and enable an increased amount of observables to be reproduced using a single force field for a series of chemically similar molecules (e.g. oligomer hydrocarbons). Specifically, we present a new optimization strategy to expand the limits of existing force fields and investigate how much added error is introduced to already reproducible observables. For this purpose, we optimized the Lennard-Jones parameters of n-octane using an isolated molecule's relative conformational energies and the liquid-phase density (293.15 K) for a molecular ensemble as optimization objectives. To test the impact on other observables, additional substances and temperatures that were not part of the training set were evaluated. This evaluation includes the surface tension, viscosity and density for n-hexane, n-heptane, n-octane and n-nonane at 293.15, 315.15 and 338.15 K. We show that it is possible to expand the limits of a force field, improving its overall accuracy at a small cost to its previously well-reproduced observables. Additionally, we propose approaches for further developments of the optimization strategy to increase the observables accuracies that suffer a loss in exchange for the capability of reproducing additional properties.