Universal Machine Learning Kohn-Sham Hamiltonian for Materials
This provides an efficient framework for computing electronic properties in materials science, potentially accelerating materials design across diverse systems like multi-elemental materials and solid-state electrolytes.
The researchers tackled the computational demands and scalability limitations of density functional theory (DFT) by developing a universal machine learning Kohn-Sham Hamiltonian model trained on DFT data from nearly all crystal structures in the Materials Project, enabling prediction of electronic structures across the entire periodic table and identifying 3,940 crystals with direct band gaps and 5,109 with flat bands in high-throughput calculations.
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 ML 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 (MOFs). Moreover, we utilize the universal model to conduct high-throughput calculations of electronic structures for crystals in GeNOME datasets, identifying 3,940 crystals with direct band gaps and 5,109 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.