  The proper choice of collective variables (CVs) is central to biased-sampling
free energy reconstruction methods in molecular dynamics simulations. The
PLUMED 2 library, for instance, provides several sophisticated CV choices,
implemented in a C++ framework; however, developing new CVs is still time
consuming due to the need to provide code for the analytical derivatives of all
functions with respect to atomic coordinates. We present two solutions to this
problem, namely (a) symbolic differentiation and code generation, and (b)
automatic code differentiation, in both cases leveraging open-source libraries
(SymPy and Stan Math respectively). The two approaches are demonstrated and
discussed in detail implementing a realistic example CV, the local radius of
curvature of a polymer. Users may use the code as a template to streamline the
implementation of their own CVs using high-level constructs and automatic
gradient computation.
