You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
When trying to convert a torsiondrive result class into a new restrained optimization dataset we lose some molecules and optimizations due to the chose of the index when a molecule is present more than once in a torsiondrive dataset due to it having multiple rotatable bonds. Maybe it would be best to change the conversion to use the original torsiondrive index and then tag it in the normal way with an index?
This could also simplify the construction of the dataset, currently, all restrained optimizations are separate entries in the dataset but we should get the same result by using all of the final geometries as starting geometries to an optimization and just freezing the dihedral angle. QCSubmit would then automatically generate the correct index for the dataset at submission time. This would lose the information on the intended torsion angle of the optimization as the constraint is more general but we could insert this into the attributes dict with the entry.
The text was updated successfully, but these errors were encountered:
jthorton
changed the title
Lossing molecules when converting torsiondrive datasets to optimizations
Losing molecules when converting torsiondrive datasets to optimizations
Dec 18, 2020
When trying to convert a torsiondrive result class into a new restrained optimization dataset we lose some molecules and optimizations due to the chose of the index when a molecule is present more than once in a torsiondrive dataset due to it having multiple rotatable bonds. Maybe it would be best to change the conversion to use the original torsiondrive index and then tag it in the normal way with an index?
This could also simplify the construction of the dataset, currently, all restrained optimizations are separate entries in the dataset but we should get the same result by using all of the final geometries as starting geometries to an optimization and just freezing the dihedral angle. QCSubmit would then automatically generate the correct index for the dataset at submission time. This would lose the information on the intended torsion angle of the optimization as the constraint is more general but we could insert this into the attributes dict with the entry.
The text was updated successfully, but these errors were encountered: