Organic Semiconductor

 

Organic semiconductors combine traditional electronics with the flexibility of organic materials, enabling innovations like wearable devices and advanced displays.

Crystallizable organic semiconductors (COS) form well-ordered crystalline structures, which improve charge transport properties. The molecules in COS are arranged in a regular pattern, allowing for more efficient charge movement.

Scientists have used machine learning (ML) to quickly identify promising COS materials. They created two ML models: one predicts the melting temperature, and the other predicts the enthalpy of melting. These properties help narrow down potential materials by estimating crystallization driving force, a key factor in crystal growth.

Instead of conducting countless experiments, researchers trained ML models to guide molecule selection. This approach reduced the pool of candidates, focusing on 13 promising molecules.

COS materials offer a valuable platform for studying charge transport in organic systems. By exploring the relationship between molecular packing, crystal structure, and electronic properties, scientists can design better organic electronics.