Optimization of the GAFF force field to describe liquid crystal molecules: the path to a dramatic improvement in transition temperature predictions
Literature Information
Nicola Jane Boyd, Mark R. Wilson
The physical properties and phase transitions of thermotropic liquid crystals are highly sensitive to small changes in chemical structure. However, these changes are challenging to model, as both the phase diagram and mesophase properties obtained from fully atomistic simulations are strongly dependent on the force field model employed, and the current generation of chemical force fields has not proved accurate enough to provide reliable predictions of transition temperatures for many liquid crystals. This paper presents a strategy for improving the nematic clearing point, TNI, in atomistic simulations, by systematic optimization of the General Amber Force Field (GAFF) for key mesogenic fragments. We show that with careful optimization of the parameters describing a series of liquid crystal fragment molecules, it is possible to transfer these parameters to larger liquid crystal molecules and make accurate predictions for nematic mesophase formation. This new force field, GAFF-LCFF, is used to predict the nematic–isotropic clearing point to within 5 °C for the nematogen 1,3-benzenedicarboxylic acid,1,3-bis(4-butylphenyl)ester, an improvement of 60 °C over the standard GAFF force field.
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Physical Chemistry Chemical Physics

Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.










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