Abstract
Accurate life cycle assessment (LCA) depends on robust characterization factors (CFs), which quantify impacts such as ecotoxicity through the integration of fate (FF), exposure (XF), and effect (EF) factors. While databases such as USEtox and Ecoinvent provide essential CFs, significant data gaps remain, particularly in ecotoxicity endpoints like hazardous concentration 50% (HC_50), which directly inform effect factor calculations. Existing machine learning models can predict such values, but they often lack interpretability, which limits trust and transparency in environmental modeling. To address this, a machine learning framework is applied that utilizes symbolic regression (SR) and genetic programming (GP) to predict missing HC_50 values from physicochemical descriptors. A dataset with 14 descriptors was used to train SR models capable of generating interpretable mathematical expressions that link chemical properties to HC_50 values. SR models were benchmarked against prominent black-box models such as random forest (RF) and neural network (NN) models. SR performance was consistent across multiple parameter configurations, while revealing recurring patterns in variable selection. These results demonstrate that symbolic regression can both predict ecotoxicity values with comparative accuracy and provide transparent functional relationships, thereby filling existing data gaps in characterization factors needed for a complete LCA.