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While halide perovskites attract significant academic attention, examples ofat-scale industrial production are still sparse. In this perspective, we reviewpractical challenges hindering the commercialization of halide perovskites, anddiscuss how machine-learning (ML) tools could help: (1) active-learningalgorithms that blend institutional knowledge and human expertise could helpstabilize and rapidly update baseline manufacturing processes; (2) ML-poweredmetrology, including computer imaging, could help narrow the performance gapbetween large- and small-area devices; and (3) inference methods could helpaccelerate root-cause analysis by reconciling multiple data streams andsimulations, focusing research effort on areas with highest probability forimprovement. We conclude that to satisfy many of these challenges, incremental-- not radical -- adaptations of existing ML and statistical methods areneeded. We identify resources to help develop in-house data-science talent, andpropose how industry-academic partnerships could help adapt "ready-now" MLtools to specific industry needs, further improve process control by revealingunderlying mechanisms, and develop "gamechanger" discovery-oriented algorithmsto better navigate vast materials combination spaces and the literature.

Opportunities for Machine Learning to Accelerate Halide Perovskite Commercialization and Scale-Up