Search Results for author:
Found 4 papers, 0 papers with code
Interpretable and Explainable Machine Learning for Materials Science and Chemistry
While the uptake of data-driven approaches for materials science and chemistry is at an exciting, early stage, to realise the true potential of machine learning models for successful scientific discovery, they must have qualities beyond purely predictive power.
BIG-bench Machine Learning
Interpretable Machine Learning
+1
Semantic based model of Conceptual Work Products for formal verification of complex interactive systems
As a first step, we illustrate how graphical class and state diagrams from UML can be developed and critiqued with subject matter experts to serve as specifications of the conceptual work product of case management.
Machine Learning and Big Scientific Data
After a brief review of some initial applications of machine learning at the Rutherford Appleton Laboratory, we focus on challenges and opportunities for AI in advancing materials science.
