Accelerating Development of Materials with Artificial Intelligence and Machine Learning

The development of novel materials and manufacturing processes can be time consuming and expensive due to the costs of experiments and the complexity of hierarchical process-structure-property-performance (PSPP) relationships that are unique within materials classes (e.g., polymers, metals, ceramics). Artificial intelligence (AI)-driven materials design is particularly useful on 1) optimization exercises that are high-dimensional, 2) problems where PSPP cause-and-effect are unknown, and 3) projects with resource constraints. While a researcher using traditional methods typically estimate which inputs are the most important to vary and develop experiments accordingly, AI can systematically create machine learning (ML) models using all input combinations and then act as a copilot for the researcher, suggesting a series of experiments to explore the design space in the most efficient manner. This talk will explore critical questions in the use of AI/ML in materials development, including how to assess performance of these methods in design exercises, what kind of data infrastructure can help in data-driven design, and whether you need complex ML models in the first place.