@mmoderwell

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

The accurate modeling of spin-orbit coupling (SOC) effects in diverse complex systems remains a significant challenge due to the high computational demands of density functional theory (DFT) and the limited transferability of existing machine-learning frameworks. This study addresses these limitations by introducing Uni-HamGNN, a universal SOC Hamiltonian graph neural network that is applicable across the periodic table. By decomposing the SOC Hamiltonian into spin-independent and SOC correction terms, our approach preserves SU(2) symmetry while significantly reducing parameter requirements. Based on this decomposition, we propose a delta-learning strategy to separately fit the two components, thereby addressing the training difficulties caused by magnitude discrepancies between them and enabling efficient training. The model achieves remarkable accuracy (mean absolute error of 0.0025 meV for the SOC-related component) and demonstrates broad applicability through high-throughput screening of the GNoME dataset for topological insulators, as well as precise predictions for 2D valleytronic materials and transition metal dichalcogenide (TMD) heterostructures. This breakthrough eliminates the need for system-specific retraining and costly SOC-DFT calculations, paving the way for rapid discovery of quantum materials.

~1900 materials collected from NovoMag and Novamag datasets, cleaned CSV with CIF file and MAE value

This paper describes the open Novamag database that has been developed for the design of novel Rare-Earth free/lean permanent magnets. The database software technologies, its friendly graphical user interface, advanced search tools and available data are explained in detail. Following the philosophy and standards of Materials Genome Initiative, it contains significant results of novel magnetic phases with high magnetocrystalline anisotropy obtained by three computational high-throughput screening approaches based on a crystal structure prediction method using an Adaptive Genetic Algorithm, tetragonally distortion of cubic phases and tuning known phases by doping.

This dataset includes 3819 materials scraped from https://magmat.herokuapp.com/, the Magnetic Materials Database. See https://www.novomag.physics.iastate.edu/structure-database for citations and more resources. I've cleaned the dataset to include the available magnetic materials (in CIF format) and their properties: magnetic_ordering, total_magnetic_moment [μ_B/cell], averaged_magnetic_moment [μ_B/atom], magnetic_polarization [T], formation_energy_(vs._elemental_phases) [meV/atom], formation_energy_above_hull [meV/atom], magnetic_curie_temperature [K], magnetic_anisotropy_constant,_k_^a-c [MJ/m^3], magnetic_easy_axis, magnetic_hardness_parameter,_κ, magnetic_anisotropy_constant,_k_^b-c [MJ/m^3], magnetic_anisotropy_constant,_k_^b-a [MJ/m^3], magnetic_anisotropy_constant,_k_^d-a [MJ/m^3]

A Computational High-Throughput Study Lorenzo A. Mariano, Vu Ha Anh Nguyen, Valerio Briganti, and Alessandro Lunghi Journal of the American Chemical Society 2024 146 (49), 34158-34166 DOI: 10.1021/jacs.4c14076

About 250mb when unzipped. Columns are mp-id and Orb v2 features of the material at ground state. Generated with https://github.com/ourofoundation/materials/blob/main/experiments/magnetism/dim-red/features_dataset.py

, a ferrimagnetic material from Materials Project. https://next-gen.materialsproject.org/materials/mp-21666

CIF file for ZrFe12Si2B, a ferrimagnetic materials from Materials Project. https://next-gen.materialsproject.org/materials/mp-653838

A 3D interactive scatter plot of magnetic materials from Materials Project. Points are colored by the materials estimated magnetic density and poitioned by UMAP reduction of Orb v2 model latent space

Room-temperature ferromagnets are high-value targets for discovery given the ease by which they could be embedded within magnetic devices. However, the multitude of potential interactions among magnetic ions and their surrounding environments renders the prediction of thermally stable magnetic properties challenging. Therefore, it is vital to explore methods that can effectively screen potential candidates to expedite the discovery of novel ferromagnetic materials within highly intricate feature spaces. To this end, the authors explore machine-learning (ML) methods as a means to predict the Curie temperature (Tc) of ferromagnetic materials by discerning patterns within materials databases.