Stop spending weeks wrangling raw database exports. MatCraft gives you 205,000+ materials with 30+ clean, normalised properties accessible via REST API, CSV, or JSON.
MP, AFLOW, and JARVIS use different property names, units, and null conventions. Harmonising them for a training set takes weeks.
Without community-standard splits, benchmark comparisons across papers are meaningless — everyone uses different subsets.
You need band gap, formation energy, elastic moduli, and magnetic moment — but no single database has all four for the same set of materials.
205,000+ entries with consistent property names, SI units, and explicit nulls. Ready to load into PyTorch or TensorFlow.
Band gap, formation energy, elastic moduli, density, crystal system, space group, and more — all in one response payload.
Pull individual materials via REST API or export filtered datasets as CSV or JSON for offline training.
Documented endpoints with Python and cURL examples. Returns JSON with full property payloads. Paginated bulk access supported.
Band gap, formation energy, bulk modulus, shear modulus, Poisson ratio, density, magnetic moment, and more.
Filter by any property combination and export the resulting dataset. Ideal for training GNNs and transformers.
Track which database version your training set came from. Reproducible benchmarks require reproducible data sources.
We trained our graph neural network on a MatCraft export and got our first publishable results in a week. Normally the data pipeline alone takes a month.
Real Stripe-live prices. Every credit pack is valid 12 months; every subscription rolls 30 days and cancels any time. Stripe-hosted checkout — we never see a card number.
Benchmark a proof-of-concept GNN on a real dataset before asking for budget.
Modal tier for a postdoc or PhD training a property predictor. $49 beats the GPU-hour bill of rerunning a MP pull.
When your benchmark requires the full 205k corpus — or your lab is running 3+ predictors against our data.
Join thousands of researchers and engineers already using MatCraft to accelerate materials discovery.