Visualization of superconducting materials

Matasov, Anton V.; Krasavina, Varvara

doi:10.1007/s42452-020-03260-6

Cited by 4 publications

(6 citation statements)

References 24 publications

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“…Once again, AI techniques can provide their efficient performance to avoid huge computation burdens and costly experiments. These techniques can be applied to predict electromagnetic, mechanical, physical, and chemical behaviour of SCs, in an ultra-fast manner [254][255][256][257][258]. In fact, a data bank of electromagnetic, mechanical, physical, and chemical properties of existing materials and SCs has been established to provide an opportunity to use AI techniques to analyse them and predict the critical temperature and properties of any new compounds.…”

Section: Materials Properties Of Scsmentioning

confidence: 99%

Artificial intelligence methods for applied superconductivity: material, design, manufacturing, testing, operation, and condition monitoring

Yazdani-Asrami

Sadeghi

Song

et al. 2022

Supercond. Sci. Technol.

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More than a century after the discovery of superconductors (SCs), numerous studies have been accomplished to take the advantage of SCs in physics, power engineering, quantum computing, electronics, communications, aviation, health care, and defence-related applications. However, there are still challenges that hinder the full-scale commercialization of SCs, such as the high cost of superconducting wires/tapes, technical issues related to AC losses, the structure of superconducting devices, the complexity and high cost of the cooling systems, the critical temperature, and manufacturing related issues. In the current century, massive advancements have been achieved on artificial intelligence (AI) techniques by offering disruptive solutions to handle engineering problems. Consequently, AI techniques can be implemented to tackle those challenges facing superconductivity and act as a shortcut towards full commercialization of SCs and their applications. AI approaches are capable of providing fast, efficient, and accurate solutions for the technical, manufacturing, and economic problems with a high level of complexity and nonlinearity in the field of superconductivity. In this paper, concept of AI and the widely used algorithms are first given. Then a critical topical review is presented for those conducted studies that used AI methods for improvement, design, condition monitoring, fault detection and location of superconducting apparatuses in large scale power applications, as well as the prediction of critical temperature and the structure of new SCs, and any other related applications. The topical review are presented in three main categories, AI for large-scale superconducting applications, AI for superconducting materials, and AI for physics of superconductors. In addition, the challenges to apply AI techniques to the superconductivity and its applications are given. Eventually, future trends on how to integrate AI techniques with superconductivity towards the commercialization are discussed.

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Section: Materials Properties Of Scsmentioning

confidence: 99%

Artificial intelligence methods for applied superconductivity: material, design, manufacturing, testing, operation, and condition monitoring

Yazdani-Asrami

Sadeghi

Song

et al. 2022

Supercond. Sci. Technol.

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“…Across various studies 4 , 38 , 45 , including the current study, researchers have discovered that the thermal conductivity stands out as the most important feature among different features in determining the T c of superconducting materials. Theoretically, the thermal conductivity of superconductors provides significant clues about the nature of their charge carriers, phonons, and the scattering processes occurring between them 46 .…”

Section: Resultsmentioning

confidence: 72%

“…The significance of thermal conductivity is directly connected to the concentration of particles capable of transferring heat 45 , 47 . The concentration of the superconducting particles (n s ) is related to a characteristic length describing the superconducting state, namely, the London penetration depth (λ), , where m, q, n s are mass, charge and concentration of superconducting particles respectively and μ 0 is magnetic constant 38 , 48 . The transition temperature of a superconductor is associated with both the London penetration depth and the coherence length.…”

Section: Resultsmentioning

confidence: 99%

“…In the second step, all the features with absolute Pearson correlation criterion values greater than 0.80 are grouped into distinct clusters. The Pearson correlation criterion is a parametric statistical method that allows to determine the existence or absence of a linear relationship between two quantitative variables 38 . This step categorizes 304 features into 62 clusters and retains 18 features with correlations less than 0.80 for the following steps.…”

Section: Methodsmentioning

confidence: 99%

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Predicting superconducting transition temperature through advanced machine learning and innovative feature engineering

Gashmard,

Shakeripour,

Alaei

2024

Sci Rep

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Superconductivity is a remarkable phenomenon in condensed matter physics, which comprises a fascinating array of properties expected to revolutionize energy-related technologies and pertinent fundamental research. However, the field faces the challenge of achieving superconductivity at room temperature. In recent years, Artificial Intelligence (AI) approaches have emerged as a promising tool for predicting such properties as transition temperature (Tc) to enable the rapid screening of large databases to discover new superconducting materials. This study employs the SuperCon dataset as the largest superconducting materials dataset. Then, we perform various data pre-processing steps to derive the clean DataG dataset, containing 13,022 compounds. In another stage of the study, we apply the novel CatBoost algorithm to predict the transition temperatures of novel superconducting materials. In addition, we developed a package called Jabir, which generates 322 atomic descriptors. We also designed an innovative hybrid method called the Soraya package to select the most critical features from the feature space. These yield R2 and RMSE values (0.952 and 6.45 K, respectively) superior to those previously reported in the literature. Finally, as a novel contribution to the field, a web application was designed for predicting and determining the Tc values of superconducting materials.

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“…A variety of different techniques have been developed over the years: PCA (or SVD), random projections, MDS, etc. [17]. However, our calculation with superconductors indicate that the best results are achieved when using a nonlinear dimensionality reduction technique called t-distributed Stochastic Neighbor Embedding (t-SNE) [18].…”

Section: Dimensionality Reduction and Data Visualizationmentioning

confidence: 99%