What R&D teams use Matforgelab for.
The prediction workflow is the same across material classes. What changes is the property target, the composition space, and which characterization files feed back into the model. Choose a vertical to see what that looks like in practice.
Predict cathode capacity and cycle-life targets before committing synthesis budget.
Lithium-ion and solid-state cathode campaigns typically involve 10 to 50 candidate compositions per R&D cycle. Matforgelab ranks them by predicted specific capacity and cycle-life stability so you synthesize the highest-probability candidates first, not a random subset. EIS-derived ionic conductivity data for electrolyte candidates is ingested directly, closing the electrochemical feedback loop after each characterization run. The cycle-life prediction carries a confidence interval: wide intervals flag candidates where the model is uncertain and physical cycling is the only path to validation.
Request AccessFormulate for Tg, elongation, and chemical resistance without running every combination.
Polymer formulation space is high-dimensional: monomer ratios, crosslinker loading, and additive type interact non-linearly, making grid search impractical beyond three or four variables. Matforgelab's GNN predicts glass transition temperature, tensile elongation, and solvent resistance across the full formulation space and identifies the Pareto frontier for multi-property trade-offs. DSC thermograms and stress-strain data from your lab feed directly back into the model, updating the property surface with each characterization batch.
Request AccessMap composition to fatigue life and yield strength without running the full ASTM battery on every alloy.
Alloy development for aerospace and structural applications requires sweeping a wide composition space against fatigue life, yield strength, and corrosion resistance specifications simultaneously. Running the full ASTM test battery on every candidate is time-prohibitive. Matforgelab maps the composition-to-property surface and shortlists the highest-probability candidates for first-round coupon testing. XRD phase analysis data from those coupons then sharpens the model's predictions for subsequent nomination rounds.
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