Protonlarn

Protonlarn is a theoretical software framework and domain-specific language designed to model and learn proton transport phenomena across materials and biological systems. It combines physics-based models of diffusion and reaction kinetics with data-driven machine learning to simulate proton conduction under varied temperatures, humidities, and structural configurations. The framework aims to facilitate rapid prototyping of materials and to enable sensor-informed calibration using experimental data.

Etymology: The name is a portmanteau of 'proton' and 'learn', signaling its focus on learning from proton-related processes. It also nods to learning-based approaches in physics-informed modeling.

Protonlarn comprises three core layers: a domain-specific language (DSL) for describing proton-conduction networks, a solver back-end for solving coupled partial differential equations and stochastic processes, and a machine-learning module for parameter estimation, model calibration, and surrogate modeling. It supports integration with Python, provides interfaces to standard scientific libraries, and includes tools for uncertainty quantification and visualization.

Development began in the early 2020s at the fictional ProtoLab initiative, with a first public release in 2023. It drew on prior work in diffusion-reaction modeling and physics-informed neural networks. Since then, community contributions have expanded its material libraries and solvers.

Applications include design and optimization of proton-exchange membranes for fuel cells, ceramic proton conductors, and biochemical proton transport studies. Researchers use Protonlarn to explore how microstructure, hydration, and defects influence conductivity, and to calibrate models against experimental impedance and tracer diffusion measurements.

Reception in the academic community has been mixed, with praise for combining physics and data-driven modeling, while concerns remain about extrapolation, data requirements, and reproducibility of DSL configurations. The project remains under active development with ongoing integrations to additional data sources.