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arXiv 2305.08038cond-mat.supr-con
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第一性原理与机器学习识别铜氧化物超导体关键配对强度因素

First Principles and Machine Learning Identify Key Pairing Strength Factors of Cuprate Superconductors

Xinyu He, Ning Chen, Jingpei Chen, Xuezhou Wang, Yang Li

AI总结:

本研究结合第一性原理能带计算与机器学习,分析了35种铜氧化物超导体的态密度峰特征,发现影响临界温度最大值的关键轨道相互作用因素包括平带及深层能带中核心轨道间的新相互作用。

摘要:

通过使用量子力学理论的能带结构计算,获得了态密度(DoS)的一些重要峰,并根据铜氧化物超导电性的晶体结构规律对其进行了分类。特别地,研究了铜氧平面内和平面外离子的轨道相互作用。从文献中收集了所有35个具有临界温度最大值数据的典型铜氧化物体系的态密度峰的位置、半宽和高度。通过对7种常见机器学习算法的训练测试,挖掘了Tc最大值与这些轨道相互作用参数之间的关系。研究发现,影响Tc最大值的轨道相互作用的关键特征不仅是平带,还有更深能带位置中核心轨道之间的一种新相互作用。

英文摘要:

By using band structure calculations of quantum mechanical theory, some important peaks of DoS (Density of States) were obtained and classified based on crystal structure laws of cuprate superconductivity. In particular, the orbital interactions of the in-plane and out-of-plane ions of the copper-oxygen plane were investigated. The position, half-width, and height of DOS peak features were collected for all 35 typical curate systems which have critical temperature maximum data from the works of literature. By training test of 7 common machine learning algorithms, the relationship between the Tc maximum values and these orbital interaction parameters were mined. It was found that the key features of the orbital interaction affecting the Tc maximum were not only the flat band but also a new interaction between core orbitals in a deeper energy band position.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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