Building Materials Knowledge Models via Document Network Analysis

Executive Summary

Our goal is to demonstrate that advances in machine learning for text can be applied to scientific literature to organize and make more accessible the knowledge that is embedded in documents and help generate new hypotheses. By focusing on literature pertaining to conversion of carbon dioxide to value-added products, we believe this work will speed development of new catalysts that reduce carbon emissions.