Designing of benzofuran-based monomers for photodetectors through similarity analysis and library enumeration

Abstract

Organic molecules have been extensively utilized in various applications including materials science, chemical, and biomedical fields. Traditionally, the design of organic molecules is achieved through experimental approaches, guided by conceptual insights, intuition, and experience. Although these experimental approaches have been successfully utilized to unveil various high-performance materials, these methods show serious limitations due to vast design space and the ever-increasing demand for organic molecules (new materials). Artificial intelligence with computer science is used by modern researchers to design materials with better performance and for predicting the properties of new materials. Herein, benzofuran-based building blocks are used as a standard molecule to search for new building blocks. Similarity analysis is performed to screen/search potential candidates for photodetectors based on the chemical structural similarity. Extended-connectivity fingerprints (ECFPs) are used for the similarity analysis. The virtual libraries of unique monomers are enumerated. The breaking retro-synthetically interesting chemical substructures (BRICS) method is also used to design building blocks by automatically decomposing and combining monomers in enumerated libraries. Moreover, this work offers a potential way to identify new monomers for photodetectors cost-effectively and rapidly.