Cai Xia, Li Yan, Liu Jianfei, Zhang Hao, Pan Jianguo, Zhan Yiqiang
College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 200234, China.
School of Information Science and Technology, Fudan University, Shanghai 200433, China.
Mater Horiz. 2023 Oct 30;10(11):5288-5297. doi: 10.1039/d3mh00967j.
Growing evidence shows that all-inorganic lead-free perovskites hold promise for solving stability and toxicity problems in perovskite solar cells. However, the power conversion efficiency of all-inorganic perovskites cannot match that of hybrid organic-inorganic perovskites. To face the challenges of efficiency, stability and toxicity simultaneously for application in perovskite solar cells, this study conducts a high-throughput materials search ensemble machine learning for nearly 12 million all-inorganic perovskites to obtain candidates with non-toxicity and excellent photovoltaic performance. Based on experimental data, models for structure identification and band gap classification are established for , and a physics-inspired multi-component neural network is proposed as part of the exploration of the model's logical structure. It is found that extracting key features for input into the model and treating non-key features as supplements make model learning easier and are more effective in reducing the model parameters. Then, based on established ensemble models as well as the new criteria of ion radius difference and the optimization rules of toxicity and cost, over 80 000 candidates are screened. Among the 34 lead-free identified with suitable band gaps and negative formation energies through first principles calculations, 17 candidates have theoretical power conversion efficiencies over 20%. The Debye temperature of 10 lead-free , basically Bi-based compounds, is greater than 350 K, which is advantageous for suppressing nonradiative recombination and thermally induced degradation.
越来越多的证据表明，全无机无铅钙钛矿有望解决钙钛矿太阳能电池中的稳定性和毒性问题。然而，全无机钙钛矿的功率转换效率无法与有机-无机杂化钙钛矿相匹配。为了同时应对钙钛矿太阳能电池应用中效率、稳定性和毒性方面的挑战，本研究对近1200万种全无机钙钛矿进行了高通量材料搜索和集成机器学习，以获得无毒且具有优异光伏性能的候选材料。基于实验数据，建立了结构识别和带隙分类模型，并提出了一种受物理启发的多组分神经网络作为对模型逻辑结构探索的一部分。研究发现，提取关键特征输入模型并将非关键特征作为补充，能使模型学习更轻松，且在减少模型参数方面更有效。然后，基于已建立的集成模型以及离子半径差的新准则和毒性与成本的优化规则，筛选出了8万多种候选材料。在通过第一性原理计算确定的34种具有合适带隙和负形成能的无铅材料中，有17种候选材料的理论功率转换效率超过20%。10种无铅材料（主要是铋基化合物）的德拜温度大于350 K，这有利于抑制非辐射复合和热致降解。
