# Computational Screening of New Perovskite Materials Using Transfer Learning and Deep Learning
## 1 Introduction  
The perovskite structure is one of the most common and widely studied structures in materials science. The general chemical formula of the perovskite compound is ABX\({}_{3}\), wherein A and B are two different sized cations, and X is an anion bonded to both. Its ideal structure is a cubic structure, and B atoms are at the center of a typical anionic octahedron. This seemingly simple atomic arrangement is deceptive because it hides its diversity of special physical and chemical properties. As a result of its rich and remarkable properties, perovskites are used in many technical fields. Examples include piezoelectric perovskites, lead zirconate titanate, perovskites with piezoelectric effect for sensors or actuators [1] and high temperature perovskite superconductors such as beryllium copper oxide [2; 3]. Some perovskite groups (mainly manganese-based perovskite oxides) exhibit huge magnetoresistance, which can significantly change the electrical resistance in the presence of a magnetic field [4]. In addition, perovskites have also been studied and applied to other fields such as thermoelectric materials [5], catalysts [6; 7], light emitting diodes (LEDs) [8; 9], lasers [10], andso on. In recent years, since perovskite can be used as an absorbent material for solar cells, it has become a research hotspot. In 2009, [PERSON] et al. [11] made CH\({}_{3}\)NH\({}_{3}\)PbBr\({}_{3}\) and CH\({}_{3}\)NH\({}_{3}\)PbI\({}_{3}\) as photosensizers for dye-sensitized solar cells for the first time, with an efficiency of 3.8%, which laid the foundation for the development of perovskite solar cells. In 2012, [PERSON] and [PERSON] [12] first used solid Spiro-OMeTAD as a hole transport layer to make a solid perovskite solar cell with an efficiency of 9.7%. In 2014, [PERSON] [13] and others from Huazhong University of Science and Technology used a full-printing method to prepare a hole-free transport layer, and a mesoporous structured perovskite solar cell using carbon as the electrode, which achieved an efficiency of 13.4%. In 2016, the Korean Institute of Chemistry (KRICT) and the Ulsan University of Science and Technology (UNIST) jointly developed a perovskite battery with an efficiency of 22.1% [14], making it the most energy-efficient perovskite solar cell. In 2019, [PERSON] et.al. [15] at Nanjing University developed a strategy to reduce tin vacancies in Pb-Sn narrow-band perovskites by a neutralization reaction, thereby improving the performance and stability of a perovskite series tandem solar cell. The large area of perovskite series tandem solar cells achieves 24.8% and 22.1% certification efficiency, respectively. Perovskite solar cell efficiency records are constantly being refreshed, and research results of perovskite solar cells continue to emerge [16]. Since perovskite materials were first used in solar cells in 2009, in just ten years, their energy conversion efficiency has reached 22.1%, far exceeding other thin film solar cells, and has broad commercial prospects. In recent years, with the accumulation of material database resources, data mining [17] and machine learning (ML) [18] have been used more and more frequently in material research, platform design and analysis, and prediction of material properties [19]. In terms of new material discovery, machine learning algorithms have been used to discover new energy materials [20], soft materials [21], polymer dielectrics [22], etc., and have achieved remarkable results. While machine learning has become a promising tool for scientists, it also has a distinct disadvantage: it usually requires a large amount of training datasets (e.g., 10\({}^{4}\)-10\({}^{6}\)). This is usually not feasible in materials science because the data set (mainly the property characterization data) for most interesting material properties, such as ion-conductivity, has approximately 10\({}^{1}\)-10\({}^{3}\) samples.