Opportunities for machine learning to accelerate halide-perovskite commercialization and scale-up

While halide perovskites attract significant academic attention, examples of industrial production at scale are still sparse. In this perspective, we review practical challenges hindering the commercialization of halide perovskites and discuss how machine-learning (ML) tools could help: (1) active-learning algorithms that blend institutional knowledge and human expertise could help stabilize and rapidly update baseline manufacturing processes, (2) computer -imaging methods with ML-based classification tools could help narrow the performance gap between large-and small-area devices, and (3) inference methods could help accelerate root-cause analysis by reconciling multiple data streams and simulations, focusing research efforts on the highest-probability areas. We conclude that to tackle many of these challenges, incremental-not radical-adaptations of existing ML methods are needed. We propose how industry -academic partnerships could help adapt ready-nowML tools to specific industry needs, further improve process control by revealing underlying mechanisms, and develop gamechangerdiscovery-oriented algorithms to better navigate the vast spaces of materials choices.

Automated summary

In this article, the practical challenges hindering the commercialization of halide perovskites are reviewed and the potential applications of machine learning in addressing these challenges are discussed. The authors propose that through the adaptation of machine learning tools in various areas, it is possible to stabilize manufacturing processes, narrow the performance gap between devices, and accelerate root-cause analysis.