MatCraft is built for anyone involved in materials development who wants to reduce the time and cost of discovering optimal compositions. The platform is designed to be accessible to domain experts who may not have a background in machine learning.
Primary Users
- Materials Scientists & Engineers: Researchers working on formulation optimization in academia or industry. MatCraft handles the ML and optimization so you can focus on domain expertise and experimental validation.
- R&D Teams: Product development groups that need to systematically explore material design spaces. MatCraft's campaign system supports collaborative workflows where multiple team members contribute data and review results.
- Computational Chemists & Physicists: Practitioners who run DFT, molecular dynamics, or other simulations. MatCraft can wrap your simulation codes as evaluation backends, then use surrogates to minimize the number of expensive runs needed.
- Data Scientists in Materials: ML practitioners who want a structured framework for Bayesian-style optimization rather than building custom pipelines from scratch.
Use Cases
- Formulating water purification membranes with optimal flux-rejection trade-offs
- Designing battery electrolyte compositions for maximum ionic conductivity
- Optimizing perovskite solar cell absorber layers for efficiency and stability
- Discovering polymer blends that balance mechanical strength and processability
- Screening catalyst compositions for selectivity and activity
Skill Level
No machine learning expertise is required for basic usage. The YAML-based configuration and guided UI walk you through setting up a campaign. Advanced users can customize surrogate architectures, write custom acquisition functions, or integrate external simulation codes via the Python SDK.