@mmoderwell

Predict how synthesizable an inorganic crystal is from its structure. Returns a Crystal-Likeness Score (CLscore) in [0, 1] using Jang et al.'s positive–unlabeled CGCNN ensemble — a soft prior complementary to energy-above-hull filters.

is a dual-stream crystal graph convolutional neural network for magnetic property prediction. It augments the full crystal graph with a magnetic subgraph that encodes metal–ligand–metal exchange geometry (Goodenough–Kanamori–Anderson rules), then predicts the DFT total magnetic moment per unit cell in μB. Saturation magnetization (Ms / μ₀ Ms) is derived from that moment and the CIF cell volume. Best for ligand-bridged magnets (oxides, nitrides, and other M–X–M systems). Not recommended for elemental metals or alloys without bridging ligands — those are out of distribution for this checkpoint. Input structures must contain at least one magnetic site (transition metal, lanthanoid, or actinoid). Paper: https://arxiv.org/abs/2606.28458 Code: https://github.com/SouravMal/mCGCNN

Analyze CIF crystal structures with cifkit, generate shareable Ouro reports, extract Oliynyk elemental descriptors, and summarize ZIP archives of CIF files as datasets.

Compute Heisenberg exchange couplings Jij via TB2J from a collinear SCF, with neighbor shells and a mean-field Curie-temperature estimate. Returns a compact JSON summary (shells, J0, Tc) plus a jij.json file with the full pair list. Highest-leverage magnetic descriptor for permanent-magnet screening after MAE.

Compute element- and orbital-resolved (projected) DOS from the cached SCF charge density. Useful for identifying which orbitals carry the moment (e.g. Fe-d), bonding character, and metallicity near the Fermi level.

Predict the electronic band gap (direct or indirect) and band-edge positions. Useful for screening semiconductors, insulators, and optoelectronic materials, and for estimating whether a structure is metallic.

Compute the DFT ground state of a crystal: total energy, magnetization, and site moments/charges. Returns a compact JSON summary plus Gaussian cube charge-density file(s). Use as a baseline energy reference or starting point for other electronic and magnetic properties.

Estimate magnetic anisotropy energy (MAE) across magnetization directions. By default, rejects DFT-unrelaxed inputs (force/stress above threshold); run /dft/structure/relax first or set allow_unrelaxed=true. Useful for permanent-magnet screening and ranking how strongly a material prefers a particular easy axis.

Compute electronic band eigenvalues across the Brillouin zone. Returns a compact JSON summary (fermi, nbands, nspin) plus a bands.json file with eigenvalues and k-points. Use for dispersion, band extrema, and carrier character in metals and semiconductors.

Compute the electronic density of states (DOS) as a function of energy. Useful for assessing metallicity, locating van Hove singularities, and comparing electronic structure across compositions or structures.

Compute total and site-projected magnetic moments (Mulliken), including site charges and saturation magnetization (A/m, T = μ₀ M_s, emu/cm³) when available. Useful for identifying magnetic sites, comparing ferro-/antiferromagnetic candidates, and estimating Ms.

Compute the real-space charge (and spin) density of the crystal. Returns Gaussian cube file(s) for visualization plus a compact JSON summary (spin channels, cube names). Useful for bonding, charge transfer, and magnetic density distributions.

Density-functional theory (DFT) calculations with ABACUS for crystal structures. Predict electronic structure (band gap, bands, density of states, charge density) and magnetic properties (moments, anisotropy) from a CIF, and optionally DFT-relax ions + cell before property evaluation. Useful for screening materials, comparing candidates, and understanding structure–property relationships.

Run an ALIGNN pretrained model on a CIF structure. Set to a model key or slug from GET /alignn/models.

Enumerate surface slabs from a bulk structure and return a zipped set of slab CIFs plus a manifest.

Generate a single candidate crystal structure for a requested formula with exact stoichiometry (e.g. SiO2 or Fe2O3). GGen chooses or validates a compatible space group, samples candidate structures, relaxes them, and returns the best result as a CIF file. Use this when you already know the target composition. To scan across stoichiometries in a chemical system (e.g. Fe-Mn-Si), use Explore a chemical system with GGen instead.

Explore a full chemical system by generating candidate crystal structures across stoichiometries, relaxing them, and ranking the results by thermodynamic stability. Use this when you want a broad discovery run for systems such as Li-Co-O or Fe-Mn-Si — not a single exact formula. For a known stoichiometry (e.g. Fe2O3), use Generate a crystal structure using GGen instead. Returns an Ouro report with a summary, selected CIFs, and an optional phase diagram.

Report the best structures known for a chemical system across the whole GGen database, published and generated alike. Where an exploration reports what a single run produced, this reports the current state of a system: its hull, how much of it comes from published data versus GGen, which phases are new, and CIFs for the leading candidates.

Screen candidate elements for a substitution template such as Fe-Bi-{X}. GGen runs shallow relaxations for each substituted chemical system and scores which elements produce near-hull or target-symmetry structures. Returns an Ouro report with the ranked systems, a summary, and CIFs for the best phase found in each system. Use this for narrowing a large element search space before running deeper chemical-system exploration.

Create an interactive phase diagram from structures already generated for a chemical system. Use this to visualize hull position, compare stable and near-hull candidates, and share the current landscape of a GGen exploration as an HTML file.