Predicting synthesis recipes of inorganic crystal materials using elementwise template formulation
Seong-Min Kim, Juhwan Noh, Geun Ho Gu, Shuan Chen, Yousung Jung
Abstract
exact match accuracy test, showing the validity of our approach for inorganic solid-state synthesis. We further validate our model by the publication-year-split test, where the model trained based on the materials data until the year 2016 is shown to successfully predict synthetic precursors for the materials synthesized after 2016. The high correlation between the probability score and prediction accuracy suggests that the probability score can be interpreted as a measure of confidence levels, which can offer the priority of the predictions.
Topics & Concepts
Materials scienceComputer scienceMachine Learning in Materials ScienceAdvanced Photocatalysis TechniquesX-ray Diffraction in Crystallography