Researchers from Carnegie Mellon University and Princeton University have developed a machine learning (ML) approach to identify crystallizable organic semiconductors (COS), materials crucial for flexible and wearable electronics. Using predictive ML models for thermal properties and crystallization driving force, they screened nearly half a million molecules, narrowing the candidates to 44 and validating six experimentally. Half of these formed platelet-shaped crystals, an ideal morphology for device applications. This interdisciplinary work combines computational and experimental methods, advancing the design of high-performance organic electronic materials and demonstrating the potential of ML-guided research in materials science.
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