The Material Measurement Lab at NIST employs artificial intelligence for the prediction and discovery of materials characteristics. Our applications of artificial intelligence (AI) accelerate materials research as well as help the community learn about AI's capabilities and gain confidence in similar applications. In addition to the projects below, we use artificial intelligence and machine learning to determine structure-property relations. Many of these projects are part of NIST's contribution to the federal multi-agency Materials Genome Initiative.
- Polymer Property Predictor and Database (PPPDB) with Natural Language Processing
- CALPHAD Uncertainty
- New use case with the Center for Hierarchical Materials Design
- The CALPHAD method is to develop phase-based property databases for multicomponent systems.
- Can AI/ML methods be used to help predict the uncertainty associated with given multicomponent assessment? (i.e. a tool to determine if the predicted liquidus temperature for commercial superalloy is within +/- 10 degrees )
- Contact Ursula Kattner
- Artificial Intelligence Self-Quality Assurance Using Learning Curves in Feedback Loops