The Motif Symmetry-Breaking Index turns binary altermagnet symmetry classification into a continuous, DFT-free design variable, enabling machine-learning discovery of candidates with spin-splitting energies up to 1.3 eV.
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Machine learning screening of Mg-containing topological quantum materials followed by DFT validation identifies Mg₂VO₄ and Mg₆MnO₈ as high-voltage cathodes with average voltages of 3.66 V and 4.06 V.
Zn substitution stabilizes ferromagnetism in EuMn2Sb2 and induces Weyl nodes near the Fermi level through broken time-reversal and inversion symmetries.
citing papers explorer
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Continuous PT-Symmetry Breaking as a Design Variable for Giant Altermagnetic Spin Splitting
The Motif Symmetry-Breaking Index turns binary altermagnet symmetry classification into a continuous, DFT-free design variable, enabling machine-learning discovery of candidates with spin-splitting energies up to 1.3 eV.
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Discovery of High-Voltage Magnesium-Ion Cathodes using Machine Learning and First-Principles Calculations
Machine learning screening of Mg-containing topological quantum materials followed by DFT validation identifies Mg₂VO₄ and Mg₆MnO₈ as high-voltage cathodes with average voltages of 3.66 V and 4.06 V.
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Broken Symmetry-driven Weyl Semimetal Phase in Zn-Substituted EuMn$_2$Sb$_2$
Zn substitution stabilizes ferromagnetism in EuMn2Sb2 and induces Weyl nodes near the Fermi level through broken time-reversal and inversion symmetries.