This case study explains how Panasonic used Citrine’s AI platform to accelerate materials discovery and develop new, patent-pending organic semiconductor molecules. To meet IoT demands for flexible, lightweight, and low-cost electronics, machine learning was applied to screen more than one million candidate molecules while performing only 196 density functional theory calculations. The approach delivered a calculated 25% increase in hole mobility for high-mobility thienoacenes and generated new insights into how molecular topology affects charge transport. By dramatically reducing computation time, improving performance, and expanding its intellectual property portfolio, Panasonic demonstrated how AI can efficiently guide innovation in organic semiconductors.

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