Artificial intelligence, machine learning, deep learning, and big data techniques for the advancements of superconducting technology: a road to smarter and intelligent superconductivity

Abstract

The last 100 years of experience within the superconducting community have proven that addressing the challenges faced by this technology often requires incorporation of other disruptive techniques or technologies into superconductivity. Artificial intelligence (AI) methods including machine learning, deep learning, and big data techniques have emerged as highly effective tools in resolving challenges across various industries in recent decades. The concept of AI entails the development of computers that resemble human intelligence. The papers published in this focus issue represent the cutting-edge and forefront research activities in the field of artificial intelligence for superconductivity.

Is Ba3In2O6 a high-Tc superconductor?

“…For this specific case our verdict is that machine learning is not more successful than the traditional means of predicting materials properties for identifying new superconductors. Nonetheless, utilizing machine learning to predict high-T c superconductors is still a comparably new approach and is constantly being improved [8,[85][86][87]. Our work indicates that bringing factors that are readily computable with first-principles computations such as band gap and the energetics of doping into these superconductor predictions would be helpful in the selection of the most promising systems in which high T c superconductivity might be experimentally realized.…”

Estimation of magnetic levitation and lateral forces in MgB2 superconducting bulks with various dimensional sizes using artificial intelligence techniques

“…Recent advancements in AI have paved the way for significant breakthroughs in high-temperature superconducting (HTS) maglev technologies. Researchers are increasingly turning to AI and machine learning (ML) techniques to tackle complex challenges in design, diagnostics, and operational control of maglev systems [20]. A notable application of AI is in the development of neural network-based state observers designed to estimate system states and parameter matrices effectively.…”