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.
|Date made available||12 Feb 2018|
- Manchester Institute of Biotechnology
- Machine learning
- Quantum chemical topology (QCT)
- Force field