The maximum carboxylation rate at 25°C (V_c,max25) plays a critical role in ecosystem models employed to comprehend and forecast the dynamics of vegetation systems. This parameter serves as a key determinant in representing the vegetation's capacity to assimilate CO₂, thus reflecting its photosynthetic potential.Previous methods of calibrating V_c,max25 and other photosynthetic parameters at individual sites encountered challenges in harnessing large-scale datasets and encountered issues such as overfitting or non-uniqueness of parameters. Here we developed a differentiable ecosystem model—a hybrid, physics-informed machine learning system— to represent the photosynthesis process within the Functionally Assembled Terrestrial Ecosystem Simulator (FATES) model. We utilized this differentiable ecosystem model to learn some parameters such as V_c,max25 and a water stress factor, from a global dataset encompassing different plant functional types (PFTs). First, we applied the traditional approach of parameterizing V_c,max25 based solely on PFTs. However, this approach lacks the ability to adequately capture inherent variability in V_c,max25 which can exhibit diverse responses to varying ambient environmental conditions. Thus, we tested the sensitivity of V_c,max25 to various environmental factors, including air temperature and radiation, over both short-term and long-term durations. Moreover, we evaluated the impact of using instantaneous measurements acquired during leaf gas exchange. To achieve this, we employed different neural network (NN) configurations to explore how they can influence the predicted V_c,max25 while employing a loss function that uses the observations of both net photosynthetic rates (A_n) and stomatal conductance (g_s). We found that parameterizing V_c,max25 using instantaneous measurements significantly improved the model performance, implying the capability of the differentiable model to improve some structural deficiencies in the process-based ecosystem model. Leveraging the flexibility of our differentiable model, we systematically explored various hypotheses to identify and address these deficiencies.