| About this Abstract |
| Meeting |
Materials Science & Technology 2020
|
| Symposium
|
Machine Learning for Discovery of Structure-Process-Property Relations in Electronic Materials
|
| Presentation Title |
Expert-guided Learning for Data-constrained Materials Science Problems |
| Author(s) |
Gopaljee Atulya, Shuyan Zhang, Alexander Davis |
| On-Site Speaker (Planned) |
Gopaljee Atulya |
| Abstract Scope |
Machine learning approaches are most successful when data is abundant to train models. In these cases, algorithms learn complicated patterns without requiring significant domain knowledge from experts. However, many materials science problems are complex (high dimensional) and data are expensive to collect, but experts have extensive knowledge that can augment purely algorithmic approaches. We discuss a framework for incorporating expert feedback in learning and optimizing algorithms for materials science problems. We combine concepts from choice modeling with machine learning models to produce novel algorithms that utilize an expert’s domain expertise as a source of data in conjunction with simulations and experiments. We explore the types of queries that extract the most information from experts when trying to learn a mapping while minimizing the cognitive burden on experts. We apply this framework to solve problems in identifying and analyzing pair distribution functions (PDF) of experimentally synthesized ceramics specifically titanium dioxide (TiO2). |