As computer simulations are increasingly used to complement or replace experiments, highly accurate descriptions of microscopic interactions are required to achieve realistic predictions. The questions of how to objectively measure model quality and how to relate the form and parameters of a force field to experimental and ab initio data are therefore of prime interest.<br/><br/>To address these issues, we use the concept of statistical distance to define a measure of similarity between a pair of statistical mechanical systems, i.e., a model and its target, and show that its maximization leads to general convergence of the model’s measurable properties to those of the target. We exploit this feature to define a rigorous basis for the development of accurate and robust effective molecular force fields that are inherently compatible with coarse-grained experimental data as well as fine-grained ab initio data from quantum chemical simulations. The new model optimization principles and their efficient implementation are illustrated through selected examples, which demonstrate higher robustness and predictive accuracy compared to other currently used methods, such as force matching.

Consistent integration of experimental and ab initio data into effective molecular force fields

253rd National Meeting (2017)

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