Since Warshel introduced the concept of electrostatic preorganization , biologists have been at odds to whether electrostatics or dynamics drive enzyme activity. Electrostatics, often measured through electric fields, 𝐸𝐸 "⃗ (𝑟𝑟), are able to drive chemical reactivity, both in biology and beyond . Enzymes are particularly interesting as they construct intrinsic electric fields that drive PyCPET's built-in benchmarking protocol allows us to probe the radius from the region of interest required for a converged field topology. For all three proteins chosen for illustration, including 30-40Å of the system from the active site to converge the streamline distribution. This is shown for cobalt-substituted Liver Alcohol Dehydrogenase (CoLADH) in Figure , where we define convergence of this metric as χ ! < 0.1 relative to the full protein and solvent. It is important to note that this is larger than the standard cutoff for long-range Coulomb interactions in MD simulations, emphasizing the relevance of long-range interactions and the sensitivity of the 𝐸𝐸 "⃗ (𝑟𝑟) topology . We justified this cutoff by looking at the response of the geometry of the quantum The convergence of the field topology metric when including charges further than 20Å for Cytochrome C heme Fe, showing the residues included at each radius marker. (E) K-medoids partitions the space of electric field topologies, with the dynamics of HIV-1 Proteas shown as an example using multidimensional scaling of the pairwise distance matrix. Bolded points are identified cluster centers. Graphs of convergence and pairwise distance matrix for other two systems studied are in Extended Data Fig. mechanical charge density, r, in the active site, and its convergence with the size of the protein region included in evaluation of electrostatics from previous work . If fewer charges provide convergence relative to the full simulation volume, the user can choose to reduce the cutoff.