The path to room-temperature superconductivity: A programmatic approach.

Room-temperature superconductivity is arguably the greatest challenge in condensed matter physics, with significant practical and commercial implications if it can be solved. There are no physical laws preventing this from occurring; indeed, superconductivity has been observed in so many different materials under so many different conditions that it is almost a "generic" property of nonmagnetic metals. This guides our viewpoint that high-temperature superconductivity is possible, if difficult to realize. Here, we lay out two grand challenges facing the field, titled the Prediction Challenge and the Engineering Challenge, and put forward a programmatic approach for overcoming them. The Prediction Challenge addresses the fact that our ability to predict new conventional superconductors has dramatically advanced in recent years, but most predicted materials are not experimentally synthesizable. To address this challenge, we propose a shift from modeling the superconducting critical temperature and dynamic stability toward high-throughput ab initio and predictive thermodynamics/synthesis modeling. The Engineering Challenge describes how we can control superconductivity with various "knobs," including pressure, nanostructuring, and light. However, our ability to predict how a specific knob will modify a given superconductor is limited, making it difficult to fully exploit them. We describe the current status and identify areas where additional work is needed to fully exploit six of the most common knobs. Progress in both of these grand challenges, while closely integrating theory and experiment into a continuous feedback loop and incorporating insights from fields beyond physics and materials science, could unlock the underlying keys to room-temperature superconductivity.

Authors

Publication Details

Journal
Apollo (University of Cambridge)
Published
2026-01-21
DOI
https://doi.org/10.17863/cam.125015
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
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article

The path to room-temperature superconductivity: A programmatic approach.

Eva Zurek, Rohit P. Prasankumar, Nathan Myhrvold, Christopher Pickard et al.
Apollo (University of Cambridge)
Machine Learning in Materials Science
article

The path to room-temperature superconductivity: A programmatic approach.

Eva Zurek, Rohit P. Prasankumar, Nathan Myhrvold, Christopher Pickard, Warren E. Pickett, Philip Kim, Bryce Meredig, Matthew Julian, Liangzi Deng, Michael Hutcheon, Timothy A. Strobel, Ching-Wu Chu, Christoph Heil, Riccardo Comin, Dmitri Basov, Stuart Wolf
article en

Abstract

Room-temperature superconductivity is arguably the greatest challenge in condensed matter physics, with significant practical and commercial implications if it can be solved. There are no physical laws preventing this from occurring; indeed, superconductivity has been observed in so many different materials under so many different conditions that it is almost a "generic" property of nonmagnetic metals. This guides our viewpoint that high-temperature superconductivity is possible, if difficult to realize. Here, we lay out two grand challenges facing the field, titled the Prediction Challenge and the Engineering Challenge, and put forward a programmatic approach for overcoming them. The Prediction Challenge addresses the fact that our ability to predict new conventional superconductors has dramatically advanced in recent years, but most predicted materials are not experimentally synthesizable. To address this challenge, we propose a shift from modeling the superconducting critical temperature and dynamic stability toward high-throughput ab initio and predictive thermodynamics/synthesis modeling. The Engineering Challenge describes how we can control superconductivity with various "knobs," including pressure, nanostructuring, and light. However, our ability to predict how a specific knob will modify a given superconductor is limited, making it difficult to fully exploit them. We describe the current status and identify areas where additional work is needed to fully exploit six of the most common knobs. Progress in both of these grand challenges, while closely integrating theory and experiment into a continuous feedback loop and incorporating insights from fields beyond physics and materials science, could unlock the underlying keys to room-temperature superconductivity.

Apollo (University of Cambridge)
Openalex Percentile: Top 83%
Machine Learning in Materials Science
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