机器学习辅助的钙钛矿X射线探测器参数分析
Machine learning aided parameter analysis in Perovskite X-ray Detector
AI总结：
本研究利用机器学习分析钙钛矿X射线探测器的15种内在性质与性能关系，发现带隙受B位金属原子序数影响，晶格长度参数对载流子迁移率-寿命乘积影响最大，并通过实验验证了模型的准确性。
摘要：
许多因素决定了钙钛矿X射线探测器的最终性能（例如灵敏度和检测限），如晶格和载流子动力学。然而，这些因素之间的关系由于材料的复杂性而未知。在本研究中，我们利用机器学习揭示卤化物钙钛矿材料的15种内在性质与其设备性能之间的关系。我们构建了一个X射线探测器数据库用于机器学习训练。结果表明，带隙主要受B位金属的原子序数影响，晶格长度参数b对载流子迁移率-寿命乘积（μτ）有最大影响。实验中生成了X射线探测器（m-F-PEA）2PbI4，进一步验证了我们ML模型的准确性。我们建议进一步研究随机森林回归在X射线探测器应用中的使用。
英文摘要：
Many factors in perovskite X-ray detectors, such as crystal lattice and carrier dynamics, determine the final device performance (e.g., sensitivity and detection limit). However, the relationship between these factors remains unknown due to the complexity of the material. In this study, we employ machine learning to reveal the relationship between 15 intrinsic properties of halide perovskite materials and their device performance. We construct a database of X-ray detectors for the training of machine learning. The results show that the band gap is mainly influenced by the atomic number of the B-site metal, and the lattice length parameter b has the greatest impact on the carrier mobility-lifetime product (μτ). An X-ray detector (m-F-PEA)2PbI4 were generated in the experiment and it further verified the accuracy of our ML models. We suggest further study on random forest regression for X-ray detector applications.
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