Design of Molecules with Low Hole and Electron Reorganization Energy Using DFT Calculations and Bayesian Optimization

Area of Science:

• Materials Science
• Computational Chemistry
• Organic Electronics

Background:

• High charge mobility materials are crucial for advanced printed electronics.
• Conventional organic semiconductors like fullerenes and fused thiophenes have limitations.
• Discovering new molecules with superior properties is an ongoing challenge.

Purpose of the Study:

• To design novel organic molecules with low reorganization energy for improved charge mobility.
• To leverage computational methods for efficient molecular discovery.
• To advance the development of materials for printed electronic applications.

Main Methods:

• Utilized density functional theory (DFT) calculations to determine molecular properties.
• Employed machine learning, specifically Gaussian process regression (GPR), for predictive modeling.
• Implemented an adaptive design of experiments (DoE) approach, iterating between GPR predictions and DFT validation over five rounds.

Main Results:

• Successfully identified new organic molecules with lower reorganization energy than existing benchmarks.
• The combined DFT and GPR approach efficiently guided the discovery process.
• Demonstrated the feasibility of using adaptive DoE for targeted molecular design.

Conclusions:

• The study successfully discovered novel organic molecules with potential for high charge mobility.
• The integrated DFT and machine learning strategy is effective for accelerating materials discovery.
• This work contributes to the development of next-generation organic semiconductors for printed electronics.