Measured against reality
Every number on this page was produced by actually running Matflow's production pipeline — never transcribed from a slide. Models scored on held-out experimental data, real physics engines benchmarked against the surrogates they replaced, six gold suites with deterministic pass gates, and standard MatBench tasks on official splits. Download anything and re-run it yourself.
Benchmark Your Own Dataset
Upload your experimental CSV — chemistry or materials, catalyst or otherwise — to run an instant 3-split validation audit (Random, Extrapolative, Cluster) with AutoML baselines and conformal calibration.
Model leaderboard
Regression quality on the held-out experimental benchmark — the Matflow ensemble balances accuracy and stability best overall.
Synthetic data fidelity
How faithfully generated rows reproduce the statistical structure of real experiments.
Laboratory case study
Four Ni-hydrotalcite catalysts calcined at 300, 600, 700 and 900 °C, dry reforming of methane, each prediction compared against the measured value from a laboratory campaign. Each output modeled independently.
The hydrotalcite and activated-carbon experiments above are published in peer-reviewed journals — ACS Omega 2025 (doi:10.1021/acsomega.5c08293) ↗ and Gas Science and Engineering 2025 (doi:10.1016/j.jgsce.2025.205716) ↗.
External validation datasets
Four independent experimental datasets — sparse synthesis data, extreme small-data, high-throughput rig, and a large operational benchmark.
Novel catalyst candidates
Top ten candidates from the optimization search — model predictions for experimental prioritization, with estimated synthesis conditions.
Benchmark datasets — download and verify
Every suite dataset is stored locally with its license recorded, so anyone can re-run the numbers without a third-party dependency. The D-Epox-Cat-ML compilation is CC-BY-4.0 (Zenodo 15750387) — please keep the attribution when you reuse it.
D-Epox-Cat-ML — cyclohexene epoxidation literature compilation
207 rows · yield target · used in the benchmark suite above
Ni-hydrotalcite dry reforming of methane (demo dataset)
51 experimental rows · 3 targets (CH4 conversion, CO2 conversion, H2/CO)
MatBench standard tasks
The official MatBench v0.1 benchmark tasks (materialsproject/matbench, MIT), run with Matflow's composition baseline on the official five-fold splits — so these scores use exactly the same test rows and metrics (MAE / ROC-AUC) as the published leaderboard. Datasets are fetched once from ml.materialsproject.org and cached locally.
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What the physics engines actually deliver
Matflow runs the real M3GNet interatomic potential (matgl, BSD-3) and the real PyBaMM continuum battery models (BSD-3) rather than curated tables. The table below compares each engine's relaxed lattice against experiment, alongside the old surrogate table — which shows ~0% error only because it contained the answers; it could not predict anything outside its rows.
Loading engine benchmark… (generated by `python -m benchmarks.engine_benchmark`)
Gold suites — the regression gates
Six versioned fixtures with deterministic pass conditions, evaluated against the production code — not a special evaluation build. DataMap 1.00 · Instrument 1.00 · Agent 1.00 · Sci-Record 1.00 · Atlas (published honestly at its real field recall — the quantified reason the human review queue exists) · base-model E2. Each card links to its full public report; every report is regenerable from the runner named on it.
Every gold suite is a versioned fixture set + runner in backend/benchmarks/ + a CI test that fails the build if a gate regresses (e.g. DataMap accuracy below 1.0, any unsafe input↔output flip, np.trapz reintroduced). Full policy on the methodology page.
Research organizations
Public organizations on the platform — opt-in profiles with a verified badge and member counts. Benchmark participation and published datasets/models land here as org-scoped work ships.
Evaluation & submissions
Signed-in users can run a model evaluation on any standardized benchmark task and submit their own (predictions, targets) pairs to be scored by Matflow against the official task format. The task catalog, scored submissions and standardized leaderboards below are public read-only views.
Run an evaluation
POST /api/benchmarks/evaluate · 5-fold cross-validation on the task datasetSubmit your model to MatBench
POST /api/evaluation/matbench/submit · scored against the official task formatRequires a signed-in session — anonymous submissions are rejected with 401. The scored result lands on the public submissions table below.
Run the numbers yourself
The Ni-hydrotalcite dry-reforming dataset (51 experimental rows) that anchors the case study above, downloadable as CSV.
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Validate your own materials
Bring your experimental data — chemistry or materials — and see Matflow's Data Synthesis → Evaluation → Prediction → Optimization pipeline produce lab-ready candidates.