Distribution coefficient prediction using multimodal machine learning based on soil adsorption factors, XRF, and XRD spectrum data

The distribution coefficient (K_d) plays a crucial role in predicting the migration behavior of radionuclides in the soil environment. However, K_d depends on the complexities of geological and environmental factors, and existing models often do not reflect the unique soil properties. We propose a multimodal technique to predict K_d values for radionuclide adsorption in soils surrounding nuclear facilities in Republic of Korea. We integrated and trained three sub-networks reflecting different data domains: soil adsorption factors for physicochemical conditions, X-ray fluorescence (XRF) data, and X-ray diffraction (XRD) spectra for inherent soil properties. Our multimodal model achieved high performance, with a coefficient of determination (R²) of 0.84 and root mean squared error (RMSE) of 0.89 for natural log-transformed K_d. This is the first study to develop a multimodal model that simultaneously incorporates inherent soil properties and adsorption factors to predict K_d. We investigated influential peaks in XRD spectra and also revealed that pH and calcium oxide (CaO) were significant variables in soil adsorption factors and XRF data, respectively. These results promote the use of a multimodal model to predict K_d values by integrating data from different domains, providing a cost-effective and novel approach to elucidate the mechanisms of radionuclide adsorption in soil.