Recently, the research group of Professor Wang Jinlan of the School of Physics of Southeast University, by combining machine learning (ML) technology and density functional theory (DFT), proposed a set of new intelligent material design strategies and successfully predicted more than 5,000 potential organic and inorganic hybrid The band gap of perovskite materials (HOIPs), and a variety of lead-free HOIPs solar cell materials with stable environment and moderate band gap have been selected from them. The research results were published online in the Nature sub-Journal "Nature Communications" (Nature Communications), titled Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning.
In the context of the energy crisis, there is an urgent need for new and efficient non-toxic solar cell materials to replace traditional fossil energy. However, the traditional material design method has the problems of low efficiency and serious waste of resources, especially when faced with thousands of candidate materials, this method is even more stretched. Recently, ML technology has emerged in the field of material design. By bypassing complex quantum mechanics, ML technology can not only greatly accelerate the design of new functional materials, but also learn the basic structure-effect relationship of materials from material data. This new material design strategy has been successfully applied in the fields of molecular organic light-emitting diodes, shape memory alloys, piezoelectric bodies, etc. However, it has not been effectively explored in the field of organic-inorganic hybrid perovskites with great photovoltaic application potential.
Based on ML technology and DFT calculation, the research group of Professor Wang Jinlan of School of Physics, Southeast University has developed a targeted driving method for discovering highly efficient and stable lead-free HOIPs. The researchers trained the ML model from 212 reported HOIPs band gap values, successfully predicted the band gap of more than 5000 potential HOIPs, and finally selected six orthogonal lead-free HOIPs with appropriate solar band gaps and room temperature thermal stability , Two of which have a direct band gap and excellent environmental stability in the visible region. The researchers also conducted big data mining through ML technology to obtain the key factors that affect the performance of ideal HOIPs solar cells. This targeted drive method overcomes the main obstacles of the traditional trial and error method. Not only can it achieve DFT accuracy in an instant, but it is particularly suitable for small data sets. This work has greatly accelerated the design process of hybrid perovskite materials with photovoltaic application potential, and can be applied to the design and discovery of other functional materials. The first author of this article is Lu Shuaihua, a first-year student of the Master's School of Physics, Southeast University. Teacher Zhou Jianhua of the School of Physics is the co-first author, and Professor Wang Jinlan is the sole corresponding author of the thesis. This work was supported by projects such as the National Key R & D Program and the National Outstanding Youth Fund. (Jiang Hongyan)
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