Hands-On Session 5: Using QM and Machine Learning Potentials to Parameterize Torsion Terms for Flexible Molecules =================================================================================================================== | Zeke Piskulich\ :sup:`1`, Timothy Giese\ :sup:`1`, and Darrin M. York\ :sup:`1` | :sup:`1`\ Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA Learning objectives ------------------- .. include:: /ModularTutorials/ForceField/parameterization_with_LigandParam/tutorial.rst :start-after: .. start-learning-objectives :end-before: .. end-learning-objectives .. include:: /ModularTutorials/ForceField/bespoke_dihedrals_with_FFPOPT/linear_scans.rst :start-after: .. start-learning-objectives :end-before: .. end-learning-objectives .. include:: /ModularTutorials/ForceField/bespoke_dihedrals_with_FFPOPT/wavefront_scans.rst :start-after: .. start-learning-objectives :end-before: .. end-learning-objectives Relevant literature ------------------- Outline ------- In this tutorial, we will be using the python package LigandParam, developed by the York Group, to parameterize a ligand for use in molecular dynamics simulations. For this tutorial, we will be parameterizing the ligand... Tutorial -------- .. include:: /ModularTutorials/ForceField/parameterization_with_LigandParam/tutorial.rst :start-after: .. start-tutorial :end-before: .. end-tutorial .. include:: /ModularTutorials/ForceField/bespoke_dihedrals_with_FFPOPT/linear_scans.rst :start-after: .. start-tutorial :end-before: .. end-tutorial .. include:: /ModularTutorials/ForceField/bespoke_dihedrals_with_FFPOPT/wavefront_scans.rst :start-after: .. start-tutorial :end-before: .. end-tutorial .. include:: /ModularTutorials/ForceField/bespoke_dihedrals_with_FFPOPT/torsional_fitting.rst :start-after: .. start-tutorial :end-before: .. end-tutorial