@inproceedings{
fox2023active,
title={Active Causal Machine Learning for Molecular Property Prediction},
author={Zachary R Fox and Ayana Ghosh},
booktitle={AI for Accelerated Materials Design - NeurIPS 2023 Workshop},
year={2023},
url={https://openreview.net/forum?id=57EslEJNOj}
}
Predicting properties from molecular structures is paramount to design tasks in medicine, materials science, and environmental management.  However, design rules derived from the structure-property relationships using correlative data-driven methods fail to elucidate underlying causal mechanisms controlling chemical phenomena. This preliminary work proposes a workflow to actively learn robust cause-effect relations between structural features and molecular property for a broad chemical space utilizing smaller subsets, entailing partial information.
Active Causal Machine Learning for Molecular Property Prediction
Zachary R Fox Ayana Ghosh