Multiple NRR reaction pathways have been proposed, including the PCET pathway (Figure ) that was used to predict the prevailing NRR scaling relations for metals. However, because the PCET pathway excludes the chemical steps of N2 adsorption and NH3* desorption, it does not consider the possibility that these steps could be rate limiting. In this work, we re-evaluate NRR scaling on metals using a more comprehensive mechanism that we call the "composite mechanism", which includes a range of proposed pathways, with both electrochemical and chemical steps. The composite mechanism includes: 1) the associative distal pathway (Figure ), 2) the associative alternating pathway (Figure ) and 3) the dissociative pathway (Figure ). The associative distal pathway is comprised of similar intermediates as the PCET pathway, but also includes N2 adsorption and NH3* desorption. The alternating pathway differs from the distal pathway in that the proximal nitrogen is protonated in an alternating sequence with protonation of the distal N. The dissociative pathway is initiated via dissociation of the N2 molecule, which is generally considered kinetically inaccessible due to the large barrier to break the N2 triple bond. However, recent work indicates that later dissociation of the nitrogen atoms may be kinetically accessible after associative adsorption and protonation to the NHNH* state, in which the N atoms are not bound through a triple bond. To model the composite NRR mechanism under fixed applied bias and solvation, we used grand canonical density functional theory (GC-DFT) to study 23 d-block metal surfaces at 0.0, -0.25, and -0.5 V vs. the standard hydrogen electrode (SHE). GC-DFT self-consistently solves the Kohn-Sham DFT equations for a system under a fixed applied potential (i.e., voltage or Fermi level) and allows the number of electrons to equilibrate self-consistently with the external potential. Surface charge is balanced by varying the ion number in an implicit electrolyte solvation model. Thus, GC-DFT treats the system as thermodynamically open with respect to the exchange of electrons and electrolyte ions with the external electron and ion reservoirs, and computes grand free energies, represented in this work as Φ. This enables a more realistic description of the electrified interface between the electrocatalyst surface and the electrolyte at electrochemical potentials set by the external potential (Figure ). Moreover, GC-DFT enables the non-PCET steps of the composite mechanism to be modeled as a function of the applied potential in contrast to neglecting them or assuming that their energetics have no potential dependence. Consequently, chemical (i.e., non-PCET) steps may still be associated with the transfer of electron density, resulting in a bias dependence of these steps. Similarly, electrochemical (i.e., PCET) steps may be accompanied by non-integer electron transfers, resulting in a bias dependence of these steps that deviates from that estimated by CHE, which assumes one electron is transferred per PCET step. Furthermore, GC-DFT has predicted changes in the binding geometries of adsorbed species with changes in applied potential that would not be captured using conventional constant charge DFT. To assess the activity of each metal surface, the energetic span approach was used, which calculates the free energy difference between the energetic trough and peak of a catalytic cycle. The energetic span is defined as the sequence of steps that lead to the most endergonic change in free energy throughout a catalytic cycle. The free energy change of the energetic span is conceptualized as the activation energy of a catalytic cycle, and under the quasi-equilibrium approximation, can be related to catalyst activity. We further simplified this approach following the work from Exner, which approximates the energetic peak of the catalytic cycle as the highest energy intermediate, avoiding the computational expense of optimizing transition state structures. Exner referred to this approximation as Gmax (i.e., maximum Gibbs free energy difference), however in this study we define the grand free energy analog, Φmax, as the Gmax approach has not previously been used alongside GC-DFT energetics. Φmax serves as a general catalytic activity screening descriptor that applies to both PCET and non-PCET steps and can be calculated at any applied potential. In this work, Φmax was calculated from GC-DFT free energy diagrams for 23 metal surfaces and found to scale linearly with the N* adatom binding energy, resulting in Φmax volcano plots. Our results predict that the chemical step of NH3* desorption is a component of the energetic span for every surface considered here, and consequently that NH3* desorption limits the thermodynamics and likely the kinetics of NRR on metal surfaces. Because NH3* desorption is a chemical step, it is less sensitive to the applied potential than PCET steps. Consequently, at more reducing potentials it becomes more limiting and truncates the Φmax volcano plot. Furthermore, we demonstrate that Φmax shifts non-linearly with bias on surfaces where the energetic span changes with bias. These results indicate the need to include both electrochemical and chemical steps when modeling NRR on electrocatalyst surfaces. This requires an approach, such as GC-DFT, that captures the potential-dependence of chemical steps. The modeling and analysis approach developed here that combines GC-DFT and Φmax can be readily applied to other electrochemical reactions and material classes. Furthermore, because this approach is computationally efficient relative to a full kinetic evaluation -that is, computing transition states for each step under an applied bias -it can be applied to high-throughput studies to enable the generation of an electrocatalyst database. The database could be used for the broad investigation of materials and reactions to accelerate the understanding of electrochemical mechanisms and the discovery of advanced materials for a range of important electrochemical conversions, including NRR to synthesize ammonia.