How does MatCraft work?

MatCraft follows a structured pipeline to go from raw material data to optimized compositions. Here is the end-to-end workflow:

1. Define Your Material

You start by creating a Material definition — either through the web UI, a YAML configuration file, or the Python SDK. A material definition specifies:

• Components: The elements or constituents that make up your material (e.g., polymer type, filler concentration, solvent ratio).
• Parameters: Continuous or discrete variables with bounds that define the search space.
• Objectives: The properties you want to optimize (e.g., maximize tensile strength, minimize permeability).
• Constraints: Hard limits that valid compositions must satisfy (e.g., component fractions must sum to 1.0).

2. Seed with Data

Import initial experimental data — as few as 10-20 data points — via CSV, JSON, or direct API calls. MatCraft uses this seed data to train an initial surrogate model.

3. Run a Campaign

A Campaign orchestrates the optimization loop. In each iteration, the surrogate model proposes candidate compositions, an acquisition function ranks them, and the top candidates are either evaluated by a physics model or flagged for experimental validation.

4. Iterate with Active Learning

The active learning loop automatically retrains the surrogate as new data arrives, refining its predictions and focusing the search on the most promising regions of the design space. Most campaigns converge in 5-15 iterations.

5. Analyze Results

The dashboard provides interactive Pareto plots, convergence charts, and exportable reports. You can compare campaigns, drill into individual candidates, and export optimized compositions for lab validation.