Structure-Property Relationships and Machine Learning for Magnetic Susceptibility Prediction in Quantum Materials: An Elastic Net Regression Approach

This study investigates the structure-property relationships and machine learning prediction capabilities for twenty quantum materials that exhibit exotic electronic and magnetic phenomena at cryogenic temperatures. The materials exhibit lattice constants ranging from 3.4 to 4.3 angstroms, electron mobilities ranging from 800-2100 cm²/V·s, band gaps of 0-0.7 eV, band sensitivities of 20-130 (×10⁻ ⁶ ), and critical temperatures ranging from 6-32 K. Statistical analysis reveals exceptionally strong positive correlations (0.93-0.97) between lattice constant, electron mobility, magnetic susceptibility, and critical temperature, while the band gap exhibits strong negative correlations (-0.84 to - 0.93) with all…