Multimodal Machine Learning Models on Aqueous Direct Photodisappearance Rate Constants of Chemicals Exhibit Previously Unattainable Performance

values for 304 chemicals. Multimodal machine learning models that simultaneously consider molecular structures and environmental conditions, such as light source, pH, temperature, dissolved oxygen level, and initial concentration of chemicals, were constructed accordingly. Results indicate that the multimodal models significantly improved the coefficient of determination on the validation set (from 0.369 to 0.756) over the unimodal models. An innovative applicability domain (AD) characterization based on feature-response landscape analysis was proposed to define the ADs of the models. The models, coupled with the AD characterization, serve as an effective tool for assessing the environmental photochemical persistence of chemicals, supporting sound chemical management.