Machine Learning-Driven Discovery and Characterization of Quantum Materials: Opportunities and Challenges

Abstract

Quantum materials represent a rapidly evolving frontier in condensed matter physics due to their extraordinary electronic, magnetic, and topological properties. These materials exhibit quantum phenomena such as superconductivity, topological protection, quantum entanglement, and correlated electron behavior, making them essential for next-generation technologies including quantum computing, spintronics, and advanced energy systems. However, the discovery and characterization of new quantum materials through traditional experimental and computational approaches remain highly resource-intensive and time-consuming. Machine Learning (ML) has emerged as a transformative computational paradigm capable of accelerating materials discovery by extracting hidden patterns from large datasets and predicting material properties with high accuracy. This paper explores the integration of machine learning techniques into quantum materials research. The study examines major ML algorithms, applications in superconductivity prediction, phase classification, and band gap estimation, along with challenges and future directions. Results indicate that ML significantly enhances predictive capability while reducing computational cost.

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