Concurrent Optimization of Device Architecture, Transport Layers, and Active Layer for Organic Photovoltaics by...

Area of Science:

• Materials Science
• Chemical Engineering
• Computational Science

Background:

• Optimizing organic photovoltaic (OPV) devices is complex due to multiscale, multiphysics interactions.
• Traditional modeling struggles with holistic device architecture and transport layer analysis.
• Machine learning (ML) has shown promise in active layer development but not for integrated device optimization.

Purpose of the Study:

• To develop a multicomponent ML framework for systematic analysis of all key OPV subsystems.
• To decode relationships between device parameters and final device efficiency.
• To establish a new paradigm for rational OPV development.

Main Methods:

• Construction of three dedicated databases: device architecture, transport layers, and active layer processing (annealing).
• Development of tailored ML models for each subsystem to predict efficiency.
• Utilizing an ensemble method for predictive accuracy and optimal configuration identification.

Main Results:

• The ML framework demonstrated strong predictive accuracy for OPV device efficiency.
• Reliable identification of optimal device configurations was achieved.
• Key relationships between subsystem parameters and efficiency were decoded.

Conclusions:

• A novel, multicomponent ML framework enables concurrent, component-wise optimization of OPV devices.
• This approach connects material selection, interfacial engineering, and macroscopic design.
• The framework facilitates the rational development of high-performance OPV devices.