Accelerated discovery of boron-dipyrromethene sensitizer for solar cells by integrating data mining and first principle

Boron-dipyrromethene (BODIPY) is one promising class of sensitizers for dye-sensitized solar cells (DSSCs) due to unique merits of high absorption coefficient and versatile structural modification capability. However, such derivatives usually suffer from limited power conversion efficiencies (PCEs) because of narrow light absorption band and low electron injection. To aid the discovery of BODIPY sensitizers, we employ an inverse design method to design efficient sensitizers by integrating data mining and first-principle techniques. We establish robust data-mining models using genetic algorithm and multiple linear regression, where the features are filtered from 5515 descriptors and their meanings are explicitly explored for next inverse designs. Based on the features’ understanding, we design candidates NH1-6 and predict their PCEs, demonstrating remarkable enhancements (58% maximum) compared to previous works. Furthermore, their optoelectronic properties including maximum absorption wavelengths, oscillator strengths, bandgaps, transferred charges, charge transferred distances, TiO2 conduction band shifts, short-circuit currents and electron injection efficiencies simulated via first-principle calculations indicate significant increasements (93 nm, 122.41%, 23.70%, 36.36%, 471.17%, 63.64%, 28.55%, 107.86% maximum), which testifies the corresponding highly predicted PCEs and may overcome BODIPY dyes’ shortcomings. The as-designed BODIPY sensitizers can be promising candidates for DSSCs, and such method could help accelerate the discovery of other energy materials.