MADField: Multi-fidelity Amortized Density Field for Adsorption in Nanoporous Materials

High-throughput computational screening of nanoporous materials for gas storage and separation requires fast and accurate characterization of adsorption equilibrium. Particle-based grand canonical Monte Carlo (GCMC) and density-based classical density functional theory (cDFT) provide simulation-based estimates of gas uptake and adsorbate density fields, but their speed-accuracy tradeoff remains insufficient for large-scale screening. In this work, we address this gap with Multi-fidelity Amortized Density Field (MADField), which reframes adsorption prediction as equilibrium density-field estimation. MADField learns from two complementary fidelities, combining broad and scalable cDFT density supervision with GCMC density labels, and recovers gas uptake by integrating the predicted density field. MADField improves uptake accuracy over the strongest baselines by 5.7× for cDFT and 3.35× for GCMC, and its predicted fields accelerate cDFT solvers with a 5.7× reduction in walltime while recovering 37.78% of cases that fail under standard settings. We also evaluate MADField under realistic screening scenarios for CH4 working-capacity and low-pressure CO2 uptake screening, where it reduces the number of required GCMC candidate evaluations to obtain the top 0.1% pool by 8.0 and 11.5 times, respectively.