MatCraft is an AI-powered materials discovery and optimization platform designed to accelerate the development of advanced materials. It provides a unified workflow for defining material compositions, running surrogate-model-driven optimization, and exploring multi-objective trade-offs — all without requiring deep expertise in machine learning or numerical optimization.
Key Features
- Surrogate Models: MatCraft trains lightweight MLP (multi-layer perceptron) neural networks on your experimental or simulation data. These surrogates approximate expensive physics calculations in milliseconds, enabling rapid exploration of vast composition spaces.
- CMA-ES Optimization: The platform uses Covariance Matrix Adaptation Evolution Strategy (CMA-ES) to intelligently search the material parameter space. CMA-ES is a derivative-free optimizer well-suited for noisy, non-convex objective landscapes common in materials science.
- Active Learning: Rather than requiring thousands of data points up front, MatCraft uses acquisition functions (Expected Improvement, Upper Confidence Bound, etc.) to suggest the most informative experiments to run next, drastically reducing the number of costly evaluations needed.
- Pareto Multi-Objective Optimization: Real materials must balance competing properties — for example, maximizing ionic conductivity while minimizing cost. MatCraft computes and visualizes Pareto fronts so you can make informed trade-off decisions.
- 16 Material Domains: Out of the box, MatCraft ships with validated domain plugins for water membranes, lithium-ion batteries, perovskite solar cells, thermoelectrics, catalysts, polymer composites, and more.
MatCraft is available as a self-hosted platform with a FastAPI backend, a Next.js frontend dashboard, a Python SDK for programmatic access, and a CLI for scripting and CI/CD integration.