PhysicsPhysical Review B
Machine learning using structural representations for discovery of high temperature superconductors
L. Novakovic, A. Salamat, et al.
This research conducted by Lazar Novakovic, Ashkan Salamat, and Keith V Lawler delves into the innovative application of machine learning to uncover high-temperature superconductors. Utilizing advanced structural representations to navigate the vast compositional phase space, the study highlights how pressure influences polymorphisms critical to superconductivity, achieving impressive accuracy in predicting transition temperatures.
Related Publications
Explore these studies to deepen your understanding
Adjacent work that informs or extends this paper's methodology and findings.
Engineering and Technology
Machine learning assisted discovery of high-efficiency self-healing epoxy coating for corrosion protection
T. Liu, Z. Chen, et al.
Medicine and Health
Discovery of senolytics using machine learning
V. Smer-barreto, A. Quintanilla, et al.
Engineering and Technology
Machine learning-guided discovery of ionic polymer electrolytes for lithium metal batteries
K. Li, J. Wang, et al.
Engineering and Technology
Accelerated discovery of high-strength aluminum alloys by machine learning
J. Li, Y. Zhang, et al.

