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We present a systematic search for wide-separation (Einstein radius >1.5"),galaxy-scale strong lenses in the 30 000 sq.deg of the Pan-STARRS 3pi survey onthe Northern sky. With long time delays of a few days to weeks, such systemsare particularly well suited for catching strongly lensed supernovae withspatially-resolved multiple images and open new perspectives on early-phasesupernova spectroscopy and cosmography. We produce a set of realisticsimulations by painting lensed COSMOS sources on Pan-STARRS image cutouts oflens luminous red galaxies with known redshift and velocity dispersion fromSDSS. First of all, we compute the photometry of mock lenses in gri bands andapply a simple catalog-level neural network to identify a sample of 1050207galaxies with similar colors and magnitudes as the mocks. Secondly, we train aconvolutional neural network (CNN) on Pan-STARRS gri image cutouts to classifythis sample and obtain sets of 105760 and 12382 lens candidates with scorespCNN>0.5 and >0.9, respectively. Extensive tests show that CNN performancesrely heavily on the design of lens simulations and choice of negative examplesfor training, but little on the network architecture. Finally, we visuallyinspect all galaxies with pCNN>0.9 to assemble a final set of 330 high-qualitynewly-discovered lens candidates while recovering 23 published systems. For asubset, SDSS spectroscopy on the lens central regions proves our methodcorrectly identifies lens LRGs at z~0.1-0.7. Five spectra also show robustsignatures of high-redshift background sources and Pan-STARRS imaging confirmsone of them as a quadruply-imaged red source at z_s = 1.185 strongly lensed bya foreground LRG at z_d = 0.3155. In the future, we expect that the efficientand automated two-step classification method presented in this paper will beapplicable to the deeper gri stacks from the LSST with minor adjustments.

HOLISMOKES -- II. Identifying galaxy-scale strong gravitational lenses in Pan-STARRS using convolutional neural networks