Use active learning and multi-objective optimization to reduce costly trial and error cycles. Our tools recommend the next best experiment based on your goals, constraints, and prior data.
Capabilities
- Suggest experiments that balance performance tradeoffs
- Continuously learn from new results to improve predictions
- Optimize formulations and processing routes
Example Applications
- Formulation optimization balancing strength, biocompatibility, and processability
- Reduce iteration cycles in superalloy materials development using predictive modelling