Work

Active Causal Machine Learning for Molecular Property Prediction

Year: 2024

Type: article

Abstract: 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.

Source: OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information)

Author: Fox, Zach [ORNL]; Ghosh, Ayana [ORNL]

Institution: Oak Ridge National Laboratory

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: green

Funders: U.S. Department of Energy, Battelle, UT-Battelle, Oak Ridge National Laboratory

Awards: G1026679911, G1645119126, G166562317, G203625172, G2988778103, G313676650, G4423657506, G5726405315, G675320460, G8906985441, G8943143067

OpenAlex ID: W4405241506

FWCI:

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