中文版 | English
题名

超导临界温度的AI模型

其他题名
THE AI MODEL OF SUPERCONDUCTING CRITICAL TEMPERATURE
姓名
姓名拼音
CHEN Pu
学号
12132029
学位类型
硕士
学位专业
070205 凝聚态物理
学科门类/专业学位类别
07 理学
导师
项晓东
导师单位
材料科学与工程系
论文答辩日期
2024-05-10
论文提交日期
2024-06-20
学位授予单位
南方科技大学
学位授予地点
深圳
摘要
目前,超导体临界温度的确定,通常需要通过经验试验的方式进行尝试和验 证,以发现新的超导材料。然而,这些实验方法不仅耗时耗力,还需要大量的人力和财力投入。因此,寻找新的方法来获取材料的性质变得迫在眉睫。
 
随着新材料的不断发现,数据量也在迅速增加。机器学习和神经网络具备处理大数据的能力,为我们提供了新的解决方案。本研究采用了深度神经网络算法、遗传算法和集成算法等先进技术,并创新性的提出了层级学习算法,从超导数据中构建模型,实现对超导材料临界转变温度的准确预测。论文首先详细介绍了超导数据的收集、整理、分类和预处理过程,以确保数据的质量和多样性。其次,针对超导材料的特性,提出了新型的描述符,包含丰富的元素信息,如原子序数、原子半径和原子质量等,用于构建机器学习模型。
 
研究结果表明,在常规超导和高温超导领域,利用机器学习算法可以有效预测超导材料的临界温度,验证集的决定系数分别达到 0.940.87 0.86,表明模型具有较好的预测效果。这些成果不仅在材料科学领域具有重要意义,还展示了人工智能与材料科学的深度融合,为加速材料研究进程提供了有效途径。
关键词
语种
中文
培养类别
独立培养
入学年份
2021
学位授予年份
2024-07
参考文献列表

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物理学
国内图书分类号
TP181
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条目标识符http://sustech.caswiz.com/handle/2SGJ60CL/765760
专题南方科技大学
工学院_材料科学与工程系
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陈朴. 超导临界温度的AI模型[D]. 深圳. 南方科技大学,2024.
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