Informatics-Guided Computational Design of Polyaromatic Hydrocarbons Suitable for Use as Hole Transport Materials

Area of Science:

• Materials Science
• Computational Chemistry
• Organic Electronics

Background:

• Polyaromatic hydrocarbons (PAHs) are crucial for optoelectronic devices.
• Efficient hole transport materials (HTMs) are needed for applications like perovskite solar cells.
• Informatics-guided design can accelerate materials discovery.

Purpose of the Study:

• To design polyaromatic hydrocarbon (PAH) molecules for optoelectronic applications.
• To identify molecules with specific energy levels (HOMO), electron blocking, and hole transport abilities.
• To optimize PAH molecules for use in perovskite solar cells.

Main Methods:

• Machine learning (ML) models were developed to predict reorganization energies and energy levels (HOMO/LUMO).
• Density Functional Theory (DFT) calculations were used for accurate property evaluation and data refinement.
• Marcus theory was applied to estimate hole transport properties.
• ML-guided selection and iterative DFT calculations accelerated the design process.

Main Results:

• ML models accurately predicted reorganization energies (~0.01 eV) and energy levels (~0.05 eV).
• A dataset of novel PAHs complementary to the COMPAS database was generated.
• Promising PAH molecules were identified with low reorganization energies (0.02 eV), wide HOMO-LUMO gaps (>2.7 eV), and optimal HOMO levels (~-5.4 eV).

Conclusions:

• Informatics-guided design effectively identifies high-performance PAH hole transport materials.
• The designed molecules are suitable for perovskite solar cells and other optoelectronic applications.
• ML and DFT integration accelerates the discovery of advanced organic electronic materials.