Abstract The high-temperature superconductor, YBa 2 Cu 3 O 7 - x (YBCO), is a promising candidate for high field magnet fabrication as it has critical temperature, T c , of over 80 K and an upper critical field over 100 T. In practical applications, the quality and stability of a superconducting magnet depends heavily on T c . Extensive research has been conducted to modify crystal structures of YBCO materials by chemical substitution and doping in order to enhance the superconducting transition temperature. The increase in T c fulfills the needs of practical applications with liquid-helium-free refrigeration and a delay in magnet failure. But the research requires significant manpower for materials synthesis, characterization, and quench detection, as well as costly equipment and facilities. In this work, the Gaussian process regression model is developed to predict YBCO superconducting transition temperature based on lattice parameters. Results here show a high correlation coefficient (99.78%) between the predicted and experimental superconducting transition temperature, a low prediction root mean square error (1.04% of the sample mean) and mean absolute error (0.27% of the sample mean), and stable model performance. This modeling approach contributes to efficient and low-cost estimations of superconducting transition temperature and understandings of the temperature based on lattice parameters.

中文翻译：

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通过高斯过程回归的钇钡铜氧化物超导转变温度建模

摘要 高温超导体 YBa 2 Cu 3 O 7 - x (YBCO) 具有超过 80 K 的临界温度 T c 和超过 100 T 的上临界场，是一种很有前景的高场磁体制造候选材料。在实际应用中，超导磁体的质量和稳定性在很大程度上取决于T c 。已经进行了广泛的研究，通过化学取代和掺杂来改变 YBCO 材料的晶体结构，以提高超导转变温度。T c 的增加满足了无液氦制冷和磁体故障延迟的实际应用需求。但这项研究需要大量人力进行材料合成、表征和淬火检测，以及昂贵的设备和设施。在这项工作中，开发了高斯过程回归模型以基于晶格参数预测 YBCO 超导转变温度。这里的结果显示预测和实验超导转变温度之间的相关系数高 (99.78%)，预测均方根误差（样本平均值的 1.04%）和平均绝对误差（样本平均值的 0.27%）低，并且稳定模型性能。这种建模方法有助于高效且低成本地估计超导转变温度以及基于晶格参数对温度的理解。低预测均方根误差（样本均值的 1.04%）和平均绝对误差（样本均值的 0.27%），模型性能稳定。这种建模方法有助于高效且低成本地估计超导转变温度以及基于晶格参数对温度的理解。低预测均方根误差（样本均值的 1.04%）和平均绝对误差（样本均值的 0.27%），模型性能稳定。这种建模方法有助于高效且低成本地估计超导转变温度以及基于晶格参数对温度的理解。