Machine Learning for Materials Discovery
Machine learning and artificial intelligence applications in science and engineering have received rapidly increasing hype over the last several years, with Citrine on the front lines of adoption of ML and AI in materials development. In this talk, I will discuss opportunities, open challenges, and recent work in materials informatics drawn from experiences on a wide range of commercial and noncommercial projects, including:
- data reuse with transfer learning,
- design of experiments with active learning,
- domain knowledge integration with graphical modeling, and
- project portfolio evaluation with design space quality metrics.).