Beyond DFT electronic band structures via dual-space orbital learning

Key points

• Accurate electronic band structures are essential for predicting material properties, but high-fidelity calculations are computationally demanding.1
• Combining PBE band energies with real- and reciprocal-space orbital matrices, O-kRystal predicts higher-level corrections for individual electronic states as a function of crystal momentum and spin.1
• For states within 5 eV of the band-edge midpoint, it predicts Heyd-Scuseria-Ernzerhof (HSE06) band energies with a mean absolute error of $0.171 \pm 0.009$ eV across nearly 6,000 three-dimensional materials spanning insulating, metallic, magnetic, and nonmagnetic systems.1
• Finally, training O-kRystal on $G0W0$ quasiparticle energies for two-dimensional materials demonstrates its applicability across levels of theory and the potential of orbital learning for higher-fidelity, momentum-resolved electronic-structure prediction beyond density functional theory.1

Sources (1)

• [1]Beyond DFT electronic band structures via dual-space orbital learning
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 10:09 AM

  We introduce O-kRystal, a dual-space orbital-learning framework that uses a lower-cost Perdew-Burke-Ernzerhof (PBE) calculation as both an energy baseline and a source of structured orbital information.

  Accurate electronic band structures are essential for predicting material properties, but high-fidelity calculations are computationally demanding.

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