Thacker, Joseph C. R. Wilson, Alex L. Hughes, Zak E. Burn, Matthew J. Maxwell, Peter I. L. A. Popelier, Paul Towards the simulation of biomolecules: optimisation of peptide-capped glycine using FFLUX <p>The optimisation of a peptide-capped glycine using the novel force field FFLUX is presented. FFLUX is a force field based on the machine-learning method kriging and the topological energy partitioning method called Interacting Quantum Atoms. FFLUX has a completely different architecture to that of traditional force fields, avoiding (harmonic) potentials for bonded, valence and torsion angles. In this study, FFLUX performs an optimisation on a glycine molecule and successfully recovers the target density-functional-theory energy with an error of 0<i>.</i>89 ± 0.03 kJ mol<sup>−1</sup>. It also recovers the structure of the global minimum with a <i>root</i>-<i>mean</i>-<i>squared deviation</i> of 0<i>.</i>05 Å (excluding hydrogen atoms). We also show that the geometry of the intra-molecular hydrogen bond in glycine is recovered accurately.</p> FFLUX;machine learning;quantum chemical topology (QCT);force field;peptide;QTAIM;kriging 2018-02-12
    https://tandf.figshare.com/articles/dataset/Towards_the_simulation_of_biomolecules_optimisation_of_peptide-capped_glycine_using_FFLUX/5877985
10.6084/m9.figshare.5877985.v1