The digital factory and lab for materials.
PhinFoundry replaces physical experiments with digital evidence. Its self-improving models learn from quantum mechanics, detect uncertainty, and generate the materials knowledge PhinOS needs to decide what deserves validation.
lower-cost simulation than traditional quantum workflows
uncertainty aware models enable continuously improving models
bottom-up model development across physical scales
Coverage across chemistry and physical scale.
PhinFoundry uses a unified model architecture for digital experimentation across chemistries, scales, and material properties.
Accurate across chemistries and material classes.
Materials coverage means expanding model accuracy across the periodic table, chemistries, surfaces, phases, and domains without rebuilding from scratch for every material family.
The system gets better when it finds uncertainty.
Self-improvement is the broader capability: PhinFoundry can identify where a prediction is uncertain, generate the right quantum data, retrain, and redeploy a stronger model. Active learning is the mechanism inside that loop.
Autonomous self-improvement
The model runs simulations, checks confidence, and triggers new simulations when it finds uncertain configurations.
Quantum-grounded accuracy
Predictions are calibrated against quantum-mechanical reference data for the structures and properties that matter.
Uncertainty-aware decisions
Per-atom uncertainty quantification identifies where the model is trustworthy and where it needs more data.
Simulate
Run model-driven simulations across candidate structures and conditions.
Measure confidence
Use uncertainty to identify where the model is extrapolating.
Generate truth data
Create targeted quantum calculations only where they improve the model.
Retrain
Fold new data back into the model family and improve coverage.
PhinFoundry generates new knowledge about matter.
Inside PhinOS workflows, PhinFoundry creates physics-grounded evidence for material questions the team has not resolved yet, shows how much to trust it, and returns the evidence PhinOS needs for the next decision.
Generate material knowledge
Create evidence about untested materials across the structures, properties, and scales that matter to the program.
Show when to trust the model
Attach confidence to predictions so teams can separate likely wins from areas that need more evidence.
Focus expensive validation
Use uncertainty to decide where additional simulation, high-throughput testing, or technical validation will create the most value.
Improve with each cycle
Fold new evidence back into the model so future searches start with better coverage and stronger predictions.
Accuracy, speed, and confidence in one loop.
PHIN combines model speed with reference calculations and uncertainty quantification, so teams can move faster without treating the model as a black box.
Accuracy
Models are trained and refined against high-fidelity quantum calculations, with R² > 0.99 on reference benchmarks.
Speed
Model inference turns long quantum workflows into second-scale screens at roughly 10,000x lower cost.
Confidence
Uncertainty estimates show when a prediction is trustworthy and when to trigger targeted reference calculations.