Crystal Structure Prediction
Run crystal structure prediction workflows from formulas, element sets, or uploaded structures.
MaterialsAtlas includes 171 apps for composition checks, crystal structure tools, property prediction, inverse design, DFT utilities, battery materials, alloys, additive manufacturing, and experimental workflows.
Start with a focused hub when you know the workflow: discovery, crystal structures, DFT setup and analysis, or candidate screening.
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.
Compare CIF structures, remove duplicates, cross-reference known libraries, filter novel candidates, and visualize structure clusters.
Explore elemental properties, trends, filters, and selected element sets for materials discovery.
Predict whether candidate compounds are likely two-dimensional materials.
Compare candidate formulas side by side across selected MToolbox predictors and Materials Project summary data.
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.
Screen alloy and high-entropy-alloy compositions using Hume-Rothery, entropy, enthalpy, VEC, and size-mismatch rules.
Predict binary or pseudobinary solid-solution miscibility gaps, binodal curves, spinodal decomposition windows, and critical temperatures.
Find thin-film substrate candidates with low lattice mismatch, orientation hints, strain state, and simple domain-matching suggestions.
Score formulas for toxicity, raw-material cost, elemental abundance, supply-chain risk, RoHS/REACH flags, and commercialization practicality.
Screen whether a material is likely to be chemically compatible with acid, base, salt solution, water, humid air, oxygen, or reducing environments.
Estimate moisture, oxygen, UV, heat, CO2, and ambient-air degradation risk for inorganic and hybrid-like material formulas.
Compute d-orbital splitting, occupancy, spin-only magnetic moment, CFSE, and a first-pass color estimate.
Estimate gamma attenuation, electron range, neutron absorption/moderation, and radiation-tolerance indicators from composition.
Estimate empirical melting points, solid-state sintering windows, and volatile-element processing flags from composition.
Analyze SEM/TEM micrographs for grain-size distribution, porosity fraction, and aspect-ratio metrics.
Measure particles, porosity, fibers/nanowires, and BSE-like phase fractions from SEM/TEM micrographs.
Parse thermal-analysis data and detect TGA mass-loss steps, DTG peaks, DSC events, and Tg candidates.
Fit Nyquist/Bode impedance spectra with common equivalent circuits and estimate ionic conductivity.
Extract direct/indirect Tauc band gaps, absorption coefficient, Urbach tail, and color estimate from UV-Vis spectra.
Plot catalyst activity or volcano score against adsorption-energy descriptors to identify near-optimal candidates.
Compare empty and intercalated host structures to estimate volume change, axis strain, and electrode degradation risk.
Normalize variable/non-stoichiometric formulas, compute mass fractions, and generate precursor weigh-out sheets.
Mask element identities in CIF/POSCAR files while preserving lattice, fractional coordinates, site ordering, and topology.
Screen ABX3 hybrid halide perovskites using tolerance factor, octahedral factor, rough band gap, and degradation warnings.
Estimate galvanic corrosion risk for two metals/alloys in contact under common electrolyte conditions.
Generate Materials Project convex-hull phase diagrams and evaluate optional user candidate phases.
Search Materials Project records and generate a shareable MToolbox passport with composition, MP data, and predicted property gaps.
Generate electrochemical stability diagrams over potential and pH for corrosion, catalysis, and aqueous battery screening.
Generate CSL bicrystal grain boundaries and basic two-surface interface stacks from CIF/POSCAR structures.
Search recent materials papers and extract evidence-backed property values into structured citation tables.
Balance synthesis reactions, generate precursor routes, and estimate screening-level thermodynamic feasibility.
Simulate powder XRD patterns from structures and compare two structures with peak-shift summaries.
Normalize, overlay, compare, and annotate XAS/XANES spectra; generate FEFF/FDMNES inputs from candidate structures.
Parse gamma-point phonon modes, classify Raman/IR activity when available, and simulate broadened spectra.
View material structures from CIF or database records.
Enumerate possible compositions from element sets and constraints.
Generate composition or structure features for machine learning workflows.
Recommend n-type and p-type dopants from a formula or CIF, with optional doped CIF generation.
Train or run machine-learning predictions from composition features.
Train or run machine-learning predictions from structure features.
Find materials with similar composition.
Find materials with similar crystal structures.
Convert between CIF and POSCAR-style structure files.
Generate supercells from uploaded structure files.
Enumerate ordered supercell configurations for partially occupied or substitutionally disordered crystals.
Prepare, validate, and package DFT input files from uploaded crystal or molecular structures.
Generate multi-step DFT workflow directories such as relax-static-bands-DOS from one uploaded structure.
Parse completed DFT output files, extract energies and convergence signals, and plot density-of-states datasets.
Upload a whole calculation folder zip or mixed output files and automatically summarize DFT results.
Create a lightweight, reproducible DFT project index that stores only key metadata and compact summaries.
Diagnose common VASP, Quantum ESPRESSO, ABINIT, GPAW, and scheduler errors from logs or zipped run folders.
Run short MACE/CHGNet molecular dynamics from uploaded structures and analyze RDF, MSD, temperature, energy drift, and stability.
Batch-relax uploaded CIF structures with MatterSim or NequIP and export relaxed CIFs plus summary tables.
Check element coverage, code-specific pseudopotential requirements, and common setup risks before submitting DFT.
Generate cutoff, k-point, and vacuum convergence-test input series from one uploaded structure.
Create reusable HPC scheduler profiles and submit scripts for DFT jobs.
Build surface-slab setup checklists and starter DFT packages from uploaded structures.
Prepare adsorption-energy calculations and compute E_ads from slab, adsorbed slab, and reference energies.
Prepare magnetic DFT starting settings, MAGMOM hints, DFT+U notes, and SOC/noncollinear guidance.
Estimate rough memory and CPU/GPU cost before submitting a DFT job.
Convert uploaded structures into starter input packages for another DFT code.
Generate high-symmetry band-structure k paths for VASP and Quantum ESPRESSO.
Recommend VASP POTCAR labels or QE/ABINIT/GPAW pseudopotential family choices by element.
Parse and plot DFT DOS, band, and projection outputs using the parser engine stack.
Analyze projected DOS, orbital channels, and PROCAR-style fat-band projection tables.
Fit electron and hole effective masses from band-edge curvature in DFT band-structure data.
Draw vacuum-level band alignments and classify Type-I, Type-II, or Type-III heterojunctions.
Compare initial and relaxed structures and report geometry changes.
Analyze completed convergence-test outputs and summarize energy changes by setting.
Generate interpolated NEB image structures between two endpoint structures.
Compute and document defect formation-energy expressions from energies, chemical potentials, and charge states.
Generate vacancy/substitution defect structure folders and a reproducible defect workflow index.
Generate ordered representative structures for partial substitutions, alloys, and solid solutions.
Generate prototype polymorphs, Bain paths, and strained structures as relaxation-ready CIF/POSCAR starting points.
Generate random-packed amorphous starting cells plus melt-quench MD schedules and ASE/MACE, VASP, or LAMMPS setup files.
Generate starter phonopy, VASP finite-displacement, and QE ph.x input files.
Parse phonopy/QE phonon outputs, flag imaginary modes, plot phonon band/DOS/thermal curves, and export mode data.
Generate finite-strain structures and static-calculation inputs for elastic tensor fitting.
Parse Bader ACF.dat outputs or generate a Bader charge-analysis checklist.
Generate simple top and bridge adsorption-site candidates from a slab structure.
Estimate work function and surface dipole metrics from vacuum/Fermi levels or potential profiles.
Fit volume-energy data to a near-minimum EOS model and estimate V0, E0, and bulk modulus.
Run a short MLIP-MD screening calculation and estimate lattice thermal-conductivity trends from a Green-Kubo-style heat-flux diagnostic.
Analyze uploaded XDATCAR, LAMMPS dump, ASE trajectory, or XYZ trajectory files for RDF, MSD, diffusion, VACF, VDOS, and coordination evolution.
Run short MLIP-MD temperature scans and mark energy/volume anomalies that suggest melting, glass transition, or solid-solid transitions.
Generate a slab from a bulk structure, relax bulk/slab with MLIP, and estimate surface energy plus a first-pass wetting proxy.
Analyze velocity trajectories, or run a short MLIP-MD trajectory, to estimate finite-temperature vibrational DOS and exploratory SED-style spectra.
Estimate steel phase fractions versus temperature, or upload a TDB database for pycalphad equilibrium.
Estimate steel transformation temperatures, CCT/TTT landmarks, nose time, and critical cooling rate.
Predict a Jominy end-quench hardness profile from steel chemistry and austenitizing conditions.
Compute carbon equivalents, cold-cracking risk, preheat guidance, and HAZ hardness risk.
Estimate austenitic steel or HEA stacking fault energy and likely TRIP/TWIP/glide deformation mode.
Compare multiple martensite-start and bainite-start temperature models from steel chemistry.
Find closest AISI/SAE, EN/DIN, JIS, and GB steel grade matches from composition ranges.
Estimate creep rupture life or stress sensitivity using Larson-Miller presets plus optional TabPFN ML for Ni-rich superalloys.
Estimate precipitate radius, volume fraction, number density, and strengthening versus aging time.
Predict grain growth and recrystallized fraction during annealing from alloy class, grain size, time, and temperature.
Estimate yield strength from composition, grain size, precipitates, dislocation density, and phase fraction.
Estimate multi-pass rolling reductions, force, torque, and power from thickness schedule and flow-stress presets.
Estimate surface and center cooling curves for simple part geometries and quench media, then compare with steel transformation temperatures.
Compute stainless PREN/CPT screening and carbon-steel CO2/H2S corrosion-rate estimates.
Recommend rough/finish rolling, cooling, and coiling/hold temperatures for target steel microstructures.
Screen steel compositions for hot tearing, cracking, segregation, and inclusion risks during continuous casting.
Predict likely oxide, sulfide, nitride, and calcium-modified inclusions from steel chemistry and melt conditions.
Estimate carburizing, nitriding, decarburizing, and simple diffusion profiles from Arrhenius diffusivity data.
Compute Cr-equivalent and Ni-equivalent for weld metal and predict stainless weld microstructure or ferrite number.
Estimate S-N fatigue life with Basquin, Goodman mean-stress correction, and Marin factors.
Predict tempered steel hardness from grade, as-quenched hardness, tempering temperature, and time.
Estimate thermal-shock resistance and cyclic thermal-stress risk from strength, conductivity, modulus, and CTE.
Predict columnar/equiaxed tendency and dendrite arm spacing from G, growth rate, and cooling rate.
Recommend practical aging temperature/time windows for precipitation-strengthened alloys.
Screen steels for hydrogen embrittlement risk from hardness, microstructure, environment, stress, and chemistry.
Estimate sliding or abrasive wear volume from hardness, load, distance, lubrication, and carbide fraction.
Estimate sheet-metal forming limits, LDR, and Erichsen index from thickness and tensile properties.
Estimate furnace time, energy consumption, and rough cost per batch or part for common heat-treatment cycles.
Mix two base metals and filler wire to estimate weld pool chemistry and Schaeffler/DeLong equivalents.
Compute rule-of-mixtures density, heat capacity, conductivity, diffusivity, CTE, and rough solidus/liquidus estimates.
Screen whether an alloy is likely printable by LPBF, DED, EBM, or WAAM from composition and process-specific risk factors.
Estimate G-R solidification morphology, dendrite arm spacing, segregation tendency, and texture for AM process conditions.
Suggest starting power, scan-speed, hatch, layer, and VED ranges for common AM process families.
Estimate melt pool width, depth, length, aspect ratio, conduction/keyhole mode, and cooling-rate metrics.
Estimate residual-stress and distortion risk from material, geometry class, and process settings.
Recommend whether supports are needed for overhangs, bridges, or holes and estimate support type, volume, and removal difficulty.
Recommend AM-specific stress relief, HIP, solutionizing, aging, and annealing sequences for common printed alloys.
Predict lack-of-fusion, keyhole, balling, and expected density risk from process parameters.
Assess powder PSD, flowability, apparent density, morphology, chemistry pickup, reuse risk, and process suitability.
Estimate top, side-wall, and down-skin roughness by build angle and recommend machining allowance.
Generate TPMS-style lattice STL files and estimate relative-density and effective-modulus scaling.
Estimate build time, powder consumption, machine cost, and post-processing cost from part size and AM settings.
Design composition gradients between alloy endpoints and flag intermetallic, volatility, and segregation risks.
Generate raster, island, contour+infill, and rotated hatch scan paths with simple thermal/stress metrics.
Apply AM-specific knockdown factors for roughness, porosity, defects, residual stress, HIP, and build orientation.