Multimodal AI framework for predicting candidate high temperature superconductors (Proposals Track)
Nidhish Sagar (Massachusetts Institute of Technology); Eslam G. Al-Sakkari (Polytechnique Montréal); Ahmed Ragab (Polytechnique Montréal)
Abstract
Materials science is at the forefront of addressing some of the most pressing challenges of our era, particularly in enhancing energy efficiency and sustainability. One of the most promising avenues in this field is the study of superconductors—materials that, when cooled below a critical temperature (Tc), exhibit zero electrical resistance. This unique property not only eliminates energy loss due to resistance but also enables a wide range of advanced technologies, such as MRI machines, magnetically levitating trains, and other high-efficiency systems. Superconductors can significantly reduce the carbon footprint of power transmission and other industrial applications. Given the complexity and importance of predicting candidate and practical high-temperature superconductors, we propose to develop a multimodal AI framework to predict new high-Tc superconducting materials. By integrating various material properties, including structural and compositional data, we seek to study patterns and relationships that could guide the discovery of new high-temperature superconductors. Success in this endeavor could significantly reduce energy losses in electrical systems, contributing to the fight against climate change.