Material science is evolving at a faster pace. Thanks to machine learning and data mining. Researchers are exploring the two methods to find efficient and cost-effective solutions. These two fields have emerged as a game-changer. These are lately being utilized in designing low band gap organic semiconductors for organic solar cells (OSCs). These are said to be rapid and computationally efficient means to identify promising materials.
Organic solar cells have gathered significant attention in recent years due to low-cost solution processing and versatility in applications. However, there are some challenges and it is the selection of suitable materials with optimal bandgap properties. The process of material discovery and optimization earlier was laborious as well as time-consuming. But, machine learning is making the process easier and fast.
The important aspect to note here is that it has the ability to utilize a plethora of data from several databases to uncover valuable insights and patterns. Researchers can extract knowledge from these sources and experimental data in data mining and thereafter can lay the groundwork for informed decision-making in material design.
The key highlight here is the comprehensive analysis of molecular descriptors, which is like building blocks for training machine learning models. By leveraging over 20 different regression models, researchers can accurately predict the properties of organic semiconductors.
The application of library enumeration and similarity analysis further enhances the efficacy of the design process. It helps in enabling the identification of high-performance semiconductor materials with tailored bandgap properties.
The fusion of data mining, machine learning and molecular design is reshaping material science. Researchers are now unlocking new frontiers with the help of big data and advanced algorithms.
