Applied machine learning for material property predictions.
Predict properties using models trained with the largest curated materials dataset.
The Challenges
- Quickly fill gaps in approved material properties and explore suitability for different applications
- Validate with enhanced confidence, new cheaper, stronger, lighter materials, more optimum conditions
- Help to hit ever decreasing material testing budget restrictions
The Solution: Total Materia Predictor
- Fill gaps in available data to drive more accurate calculations
- Selection opportunities by predicting properties for new materials
- Save money by reducing material testing costs
STEP 1
Search for a material designation to predict missing properties.
STEP 2
Select ML model that you want to use to predict the properties.
STEP 3
Select parameters such as temperature ranges, heat treatment and form - PREDICT!.
STEP 4
Assess statistical health of the prediction such as MAPE, MAE and R values.
The Benefits
Unparalleled universality
For 100K’s of materials
Integration
Saving, managing and sharing predictions
Quality and reliability
Trained with the largest dataset on the planet