Search more materials. Spend high-fidelity evidence where it matters.
LargeQuant supports quantitative materials and process discovery with composition records, surrogate models, multi-fidelity learning, high-fidelity simulation, DFT integration and closed-loop experiment planning.
The design space is large; trusted evaluations are expensive.
Materials discovery often requires navigating enormous composition/process spaces with only a small number of high-quality simulations or experiments. LargeQuant turns that imbalance into a quantitative campaign: learn from lower-cost evidence, expose uncertainty, and spend high-fidelity compute or laboratory capacity on the candidates most likely to change the decision.
Make the next high-fidelity calculation a quantitative choice.
Visualise observations, surrogate structure and the next evaluation point as one response surface instead of hiding candidate selection inside a model pipeline.
High-value quantitative workflows.
Property optimisation
Search composition and process variables against one or more target material properties.
Multi-fidelity screening
Fuse lower-cost evidence with calibrated high-fidelity evaluations.
DFT-backed discovery
Connect Quantum ESPRESSO or other trusted high-fidelity paths when provisioned.
Process-window optimisation
Explore composition and processing conditions jointly under hard constraints.
Next-experiment selection
Use surrogate uncertainty and acquisition logic to decide which candidate should be evaluated next.
Evidence-driven validation
Connect computational predictions with measured material observations and preserve the lineage between them.
Connect the domain model to the evidence and decision layer.
Quantitative infrastructure around your domain expertise.
Materials records
Composition-aware scientific entities and quantitative observations.
Gaussian-process surrogates
Prediction with variance and uncertainty intervals.
Multi-fidelity models
Combine lower-cost and high-fidelity evidence.
DFT boundary
Trusted Quantum ESPRESSO integration when installed and configured.
Experiment planning
Prioritise new evaluations and preserve result lineage.
Reproducibility
Dataset, execution, artifact and model protocol evidence.
Designed to connect to the stack you already operate.
- Materials databases and composition tables
- Simulation results and descriptor datasets
- DFT/HPC environments
- Laboratory measurement systems
- Process-condition datasets
- Property targets and engineering constraints
Built for cross-functional quantitative work.
- Materials Informatics
- Computational Materials Science
- R&D / Innovation
- Chemical & Process Engineering
- Advanced Manufacturing
- Scientific Computing
Keep the basis of the decision inspectable.
LargeQuant can use a low-fidelity model to navigate broadly while reserving high-fidelity simulation or measurement for decision-critical points. The evidence hierarchy remains visible so a surrogate estimate never silently becomes a measured property.
Explore evidence & provenance →