Deep graph kernel learning for material & atomic level uncertainty quantification in adsorption energy prediction

Deep graph kernel learning combines GNN backbones with Gaussian processes to deliver calibrated, atomic-level uncertainty quantification for adsorption energy prediction, outperforming ensemble and evidential methods for catalyst discovery.