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Abstract. Photosynthesis plays an important role in carbon,nitrogen, and water cycles. Ecosystem models for photosynthesis arecharacterized by many parameters that are obtained from limited in situmeasurements and applied to the same plant types. Previous site-by-sitecalibration approaches could not leverage big data and faced issues likeoverfitting or parameter non-uniqueness. Here we developed an end-to-endprogrammatically differentiable (meaning gradients of outputs to variablesused in the model can be obtained efficiently and accurately) version of thephotosynthesis process representation within the Functionally AssembledTerrestrial Ecosystem Simulator (FATES) model. As a genre ofphysics-informed machine learning (ML), differentiable models couplephysics-based formulations to neural networks (NNs) that learn parameterizations(and potentially processes) from observations, here photosynthesis rates. Wefirst demonstrated that the framework was able to correctly recover multiple assumedparameter values concurrently using synthetic training data. Then, using areal-world dataset consisting of many different plant functional types (PFTs), welearned parameters that performed substantially better and greatly reducedbiases compared to literature values. Further, the framework allowed us togain insights at a large scale. Our results showed that the carboxylationrate at 25 ∘C (Vc,max25) was more impactful than a factorrepresenting water limitation, although tuning both was helpful inaddressing biases with the default values. This framework could potentiallyenable substantial improvement in our capability to learn parameters andreduce biases for ecosystem modeling at large scales.