Statistical analysis and visualization of data of non-fullerene small molecule acceptors from Harvard organic photovoltaic database. Structural similarity analysis with famous non-fullerene small molecule acceptors to search new building blocks

Data-driven material design has gained the position of fourth paradigm with the first three being experiments, theory, and simulation. The statistical analysis and visualization of data can provide many information about hidden trends of data. It also can help to plan future material design in efficient way. Traditional methods need long experimental time and much money. Data driven research has become new hope to revolutionize materials discovery. A detail statistical and visualization analysis of Harvard organic photovoltaic database is performed. Various new information is found. Similarity analysis is also performed. Famous experimentally reported non-fullerene small acceptors are selected as reference molecules. Very less similarity is found that is indicating that acceptors in Harvard organic photovoltaic database are much different from acceptors that are reported in literature. The molecules in database are not suitable acceptors, however, their suitable combination can result efficient acceptors of organic solar cells. New acceptors are designed using some building block from database and their electronic properties are studied using density functional theory. Our data mining assisted material design framework is based on unique approach that is rare in photovoltaics research. This framework will be helpful to find versatile building blocks.

Automated summary

Data-driven material design has become the fourth paradigm, providing valuable insights through statistical analysis and visualization of data. It offers a more efficient approach to plan future material design and has the potential to revolutionize materials discovery.