Opportunities for machine learning to accelerate halide-perovskite commercialization and scale-up
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2110.03923v1.pdf
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Author(s)
Kumar, Rishi E.
Tiihonen, Armi
Sun, Shijing
Fenning, David P.
Liu, Zhe
Buonassisi, Tonio
Date Issued
May 2022
Journal
Matter
Publisher
Elsevier BV
Citation
Kumar, Rishi E., Tiihonen, Armi, Sun, Shijing, Fenning, David P., Liu, Zhe et al. 2022. "Opportunities for machine learning to accelerate halide-perovskite commercialization and scale-up." Matter, 5 (5).
Version
Author's final manuscript
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
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Creative Commons Attribution-Noncommercial-ShareAlike
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DOI of Published Version
https://doi.org/10.1016/j.matt.2022.04.016
