Neural Networks Predict T-c for High-Temp Superconductors
Neural Networks Predict T-c for High-Temp Superconductors
Multilayer Neural Network Models for Critical Temperature of Cuprate Superconductors
Tags: Universiti Malaya, Malaysia, Computing Technology, Energy & Environment
This research utilizes multilayer neural network models to predict the critical temperature (T-c) of cuprate superconductors based on their chemical composition and lattice parameters. The models study four superconductor systems and propose candidates for new superconductors with higher T-c values. The back-propagation algorithm enhances prediction accuracy, analyzing eight parameters in one model and eleven in the other. These models outperform previous machine learning approaches in T-c prediction. The applications include designing high-temperature superconductors for energy transmission and other advanced technologies.
IP Type or Form Factor: Discovery & Research; Software & Algorithm
TRL: 3 - proof of concept with needs validated
Industry or Tech Area: Quantum Computing & Communication; Energy Efficiency
