Crystal Structure Prediction
Run crystal structure prediction workflows from formulas, element sets, or uploaded structures.
Browse MaterialsAtlas apps for AI-assisted materials discovery, composition screening, property prediction, inverse design, candidate ranking, and open materials workflows.
These tools help researchers move from formula ideas and candidate lists to screened, ranked, and documented materials hypotheses.
These tools help researchers move from formula ideas and candidate lists to screened, ranked, and documented materials hypotheses.
The Materials Discovery Apps hub is a crawlable starting point for AI-assisted materials research. It links composition tools, property predictors, inverse-design workflows, and screening apps that help researchers move from broad chemical spaces to smaller, testable candidate lists.
Run crystal structure prediction workflows from formulas, element sets, or uploaded structures.
Filter out formulas that pass charge neutrality screening.
Check whether neutral oxidation-state assignments also satisfy a Pauling electronegativity screen.
Predict formation energy and energy-above-hull style screening values.
Calculate energy above the Materials Project convex hull from formula energy, CIF energy, or an approximate MACE-predicted energy diagnostic.
Run Pauling-style formula checks and tolerant CIF structure-risk screening.
Predict element oxidation states for formulas or CIF structures using BERTOS, pymatgen, oxi.matr.io PNAS, or TOSS.
Rank formula or CIF candidates by composition-based or structure-aware synthesizability scoring.
Filter CIF structures by ML-potential phonon dynamic-stability screening.
Predict lattice constants from formula or structure inputs.
Predict space group, crystal system, and related cell parameters.
Predict whether candidate compounds are likely two-dimensional materials.
Compare candidate formulas side by side across selected MToolbox predictors and Materials Project summary data.
Compare CIF structures, remove duplicates, cross-reference known libraries, filter novel candidates, and visualize structure clusters.
Upload measured property data, use Magpie or your own descriptor columns, and discover interpretable equations.
Predict noncentrosymmetric materials from composition or structure input.
Predict band gap values from composition or structure input.
Predict elastic moduli for candidate materials.
Estimate elastic tensors and VRH moduli from uploaded structures using fast finite-strain MLIP stress calculations.
Predict hardness-related material properties.
Predict thermal conductivity for candidate materials.
Predict room-temperature linear and volumetric thermal expansion coefficients for substrate matching and reliability screening.
Predict scalar dielectric constants for capacitor, insulator, and functional dielectric screening.
Predict composition-level piezoelectric response coefficients for sensors, actuators, and energy harvesting.
Predict refractive index, optical band gap, approximate color, absorption edge, and Shockley-Queisser photovoltaic limit.
Estimate Seebeck coefficient, electrical conductivity, thermal conductivity, power factor, and zT at 300/600/900 K.
Predict magnetic ordering, Curie temperature, or Néel temperature from composition.
Predict ionic conductivity for candidate materials.
Predict superconductivity-related properties for candidate formulas.
Paste formulas or upload a CSV, select property groups, and rank candidates with Pareto-optimal materials highlighted.
Enumerate a chemical system on a composition grid and visualize ranked candidates by selected target property.
Upload measured formula data or numeric feature columns, fit a surrogate, and recommend next candidates.
Build an interactive-style trade-off report for formulas across selected property axes and filters.
Find candidate formulas closest to a requested target property profile and show which constraints fail.
Search and screen hypothetical composition database entries.
Search hypothetical cubic material candidates.
Search superconductor CIF candidates from ICSD, Materials Project, and hypothetical libraries.
Search quantum material CIF candidates from ICSD, Materials Project, and hypothetical libraries.
Search generated two-dimensional material candidates.
Search generated lithium material candidates.
Run a combined battery-material screen for electrolytes, cathodes, or anodes.
Predict average conversion-electrode voltage, capacity, and energy density from Materials Project thermodynamics.
Estimate solid-electrolyte reduction and oxidation limits using grand-potential phase diagrams.
Explore elemental properties, trends, filters, and selected element sets for materials discovery.