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
core
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