论文标题
超导体中电子玻色子机制的机器学习
Machine learning on the electron-boson mechanism in superconductors
论文作者
论文摘要
从有限的间接实验数据中解开超导体的配对机制始终是一项艰巨的任务。它很常见，但有时可以通过具有一些调谐参数的理论模型来解释。在这项工作中，我们建议机器学习可能从超导间隙函数等可观察物中推断出配对机制。对于Migdal-Eliashberg理论中的超导性，我们在超导间隙函数和电子玻色子光谱函数之间进行了监督的学习。对于简单的光谱函数，神经网络可以轻松捕获对应关系并完美预测。对于复杂的光谱函数，使用自动编码器来降低光谱函数的复杂性，以与GAP函数兼容。在此复杂性降低过程之后，提取光谱函数的相关信息并恢复良好的性能。我们提出的方法可以从数据中提取相关信息，并且可以应用于物理或其他领域中不对称复杂性的一般功能 - 功能映射。
To unravel pairing mechanism of a superconductor from limited, indirect experimental data is always a difficult task. It is common but sometimes dubious to explain by a theoretical model with some tuning parameters. In this work, we propose that the machine learning might infer pairing mechanism from observables like superconducting gap functions. For superconductivity within the Migdal-Eliashberg theory, we perform supervised learning between superconducting gap functions and electron-boson spectral functions. For simple spectral functions, the neural network can easily capture the correspondence and predict perfectly. For complex spectral functions, an autoencoder is utilized to reduce the complexity of the spectral functions to be compatible to that of the gap functions. After this complexity-reduction process, relevant information of the spectral function is extracted and good performance restores. Our proposed method can extract relevant information from data and can be applied to general function-to-function mappings with asymmetric complexities either in physics or other fields.
