One application of artificial intelligence (AI) in materials is the acceleration of materials innovation, which is the mission of the Materials Genome Initiative. However, to decrease the cost and time-to-market, we must continuously assess the quality of models with new facts. AI quality assurance (QA) is fundamental to determine a model's inadequacy. QA must be part of any AI system to oversee its learning process and direct it toward bettering quality or alerting to a quality issue.
This work can
- Measure the accuracy of AI systems.
- Explore the adequacy of an AI models with regards to some data.
- Generally perform QA on AI systems’ learning process.