Poster
in
Workshop: AI for Accelerated Materials Design (AI4Mat-2023)
Active Causal Machine Learning for Molecular Property Prediction
Zachary Fox · Ayana Ghosh
Keywords: [ Molecular Property Prediction ] [ causal ML ] [ Active Learning ] [ Active learning ] [ molecular property prediction ]
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.