Explore embedding domain knowledge for machine learning of complex material systems in this 26-minute conference talk by Newell Washburn from Carnegie Mellon University. Delve into topics such as response surfaces, hierarchical machine learning, and the random forest approach. Understand how hidden correlations and trends in data contribute to the development of algorithms for complex material systems. Learn about the general problem, middle layer contributions, and practical examples of applying these concepts. Gain insights into the machine learning revolution in materials research, as presented at the Materials Research Society conference.
Embedding Domain Knowledge for Machine Learning of Complex Material Systems
Materials Research Society via YouTube
Overview
Syllabus
Introduction
Complex Material Systems
Response Surfaces
General Problem
hierarchical machine learning
example
how does this work
contributions
Middle layer
Algorithm
Trends in Data
Hidden Correlation
Random Forest Approach
Conclusion
Question
Taught by
Materials Research Society
