Opportunities for Machine Learning to Accelerate Halide Perovskite Commercialization and Scale-Up
This work targets the industrial commercialization of halide perovskites, but it is incremental as it focuses on applying existing ML tools rather than introducing new breakthroughs.
The paper addresses the challenge of scaling up halide perovskite production for commercialization by proposing that machine learning tools, such as active-learning algorithms and ML-powered metrology, can help stabilize processes and narrow performance gaps, though it notes that only incremental adaptations of existing methods are needed.
While halide perovskites attract significant academic attention, examples of at-scale industrial production 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) ML-powered metrology, including computer imaging, 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 effort on areas with highest probability for improvement. We conclude that to satisfy many of these challenges, incremental -- not radical -- adaptations of existing ML and statistical methods are needed. We identify resources to help develop in-house data-science talent, and propose how industry-academic partnerships could help adapt "ready-now" ML tools to specific industry needs, further improve process control by revealing underlying mechanisms, and develop "gamechanger" discovery-oriented algorithms to better navigate vast materials combination spaces and the literature.