Discovering chemically novel, high-temperature superconductors
Discovering chemically novel, high-temperature superconductors
复制标题
发现化学新颖的高温超导体
DOI：
10.1016/j.commatsci.2023.112358
复制
发表时间：
2023
影响因子：
3.3
通讯作者：
Sparks, Taylor D.
中科院分区：
文献类型：
--
作者：
Seegmiller, Colton C.;Baird, Sterling G.;Sayeed, Hasan M.;Sparks, Taylor D.
凝聚态物理中尚未解决的最大问题之一是产生高温超导的机制，以及是否有一种材料在室温和常压下都能表现出超导性。在超导体的许多重要特性中，临界温度（T c）或转变温度是材料转变为超导状态的点。在这个实现中，机器学习被用来预测化学独特化合物的临界温度，试图识别新的化学新颖的高温超导体。训练数据集（SuperCon）由已知的超导体及其临界温度组成，测试数据集（NOMAD）由大约70万个新化学式组成。这些数据集中的化学式首先通过一系列快速筛选工具SMACT来检查化学有效性。接下来，使用DiSCoVeR算法对SuperCon数据进行训练，形成模型，然后对NOMAD数据集中的公式进行批量筛选。DiSCoVeR算法结合了化学距离度量、密度感知降维、聚类和回归模型，可作为识别和评估这些超导成分的工具（Baird et al., 2022）。这项研究和实施导致了化学新成分的筛选，其临界温度高于150 K，这与铜类超导体相关。该实现演示了执行机器学习辅助超导体筛选的过程（同时探索化学上不同的空间），可用于材料发现过程。
One of the biggest unsolved problems in condensed matter physics is what mechanism causes high-temperature superconductivity and if there is a material that can exhibit superconductivity at both room temperature and atmospheric pressure. Among the many important properties of a superconductor, the critical temperature (T c) or transition temperature is the point at which a material transitions into a superconductive state. In this implementation, machine learning is used to predict the critical temperatures of chemically unique compounds in an attempt to identify new chemically novel, high-temperature superconductors. The training data set (SuperCon) consists of known superconductors and their critical temperatures, and the testing data set (NOMAD) consists of around 700,000 novel chemical formulae. The chemical formulae in these data sets are first passed through a collection of rapid screening tools, SMACT, to check for chemical validity. Next, the DiSCoVeR algorithm is used to train on the SuperCon data to form a model, and then screens through batches of the formulae in the NOMAD data set. Having a combination of a chemical distance metric, density-aware dimensionality reduction, clustering, and a regression model, the DiSCoVeR algorithm serves as a tool to identify and assess these superconducting compositions (Baird et al., 2022). This research and implementation resulted in the screening of chemically novel compositions exhibiting critical temperatures upwards of 150 K, which correlates to superconductors in the cuprate class. This implementation demonstrates a process of performing machine learning-assisted superconductor screening (while exploring chemically distinct spaces) which can be utilized in the materials discovery process.
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材料信息学揭示了铜酸盐超导体中未探索的结构空间
影响因子：
19
作者：
Rhys E. A. Goodall;Bonan Zhu;J. MacManus‐Driscoll;A. Lee
通讯作者：
A. Lee
使用机器学习根据结构和拓扑参数预测掺杂铁基超导体临界温度
DOI：
--
发表时间：
2021
期刊：
International Journal of Materials Research - Zeitschrift für Metallkunde
影响因子：
--
作者：
Yun Zhang;Xiaojie Xu
通讯作者：
Xiaojie Xu
影响因子：
9.7
作者：
Wang, Anthony Yu-Tung;Kauwe, Steven K.;Sparks, Taylor D.
通讯作者：
Sparks, Taylor D.
影响因子：
9.7
作者：
Stanev, Valentin;Oses, Corey;Takeuchi, Ichiro
通讯作者：
Takeuchi, Ichiro
DOI：
10.1007/bf01303701
发表时间：
1986-01-01
期刊：
ZEITSCHRIFT FUR PHYSIK B-CONDENSED MATTER
影响因子：
--
作者：
BEDNORZ, JG;MULLER, KA
通讯作者：
MULLER, KA