Building Ouro, using AI to search for room-temp superconductors and rare-earth free permanent magnets.
MatterGen generated crystal structures for Fe-N
Generated crystal structure for Fe-N
Relaxed with Orb v3; 0.03 eV/Å threshold; final energy = -143.6773 eV
Supercell 2x2x2 of Ba2YCu3O7 (Space group: Pmmm, 64 symmetry operations)
Relaxed with Orb v3; 0.03 eV/Å threshold; final energy = -79.7708 eV
Relaxed with Orb v3; 0.03 eV/Å threshold; final energy = -125.0215 eV
Supercell 2x2x2 of Fe6N (Space group: Cm, 32 symmetry operations)
Relaxed with Orb v3; 0.03 eV/Å threshold; final energy = -111.5653 eV
Supercell 3x3x3 of CBiFe4S
Relaxed with Orb v3; 0.03 eV/Å threshold; final energy = -44.2005 eV
While density functional theory (DFT) serves as a prevalent computational approach in electronic structure calculations, its computational demands and scalability limitations persist. Recently, leveraging neural networks to parameterize the Kohn–Sham DFT Hamiltonian has emerged as a promising avenue for accelerating electronic structure computations. Despite advancements, challenges such as the necessity for computing extensive DFT training data to explore each new system and the complexity of establishing accurate machine learning models for multi-elemental materials still exist. Addressing these hurdles, this study introduces a universal electronic Hamiltonian model trained on Hamiltonian matrices obtained from first-principles DFT calculations of nearly all crystal structures on the Materials Project. We demonstrate its generality in predicting electronic structures across the whole periodic table, including complex multi-elemental systems, solid-state electrolytes, Moiré twisted bilayer heterostructure, and metal-organic frameworks. Moreover, we utilize the universal model to conduct high-throughput calculations of electronic structures for crystals in GNoME datasets, identifying 3940 crystals with direct band gaps and 5109 crystals with flat bands. By offering a reliable efficient framework for computing electronic properties, this universal Hamiltonian model lays the groundwork for advancements in diverse fields, such as easily providing a huge data set of electronic structures and also making the materials design across the whole periodic table possible. This paper corresponds to HamGNN v1 and the universal model weights released in 2024. https://iopscience.iop.org/article/10.1088/0256-307X/41/7/077103