There is not much data in the literature regarding the ED nature of the solvents. While there is some data to back up our screening, our emphasis is on being able to screen with greater accuracy for the compounds where we don't have much data. In the face of data scarcity, the quality of such screening becomes more reliable if the associated uncertainties are smaller. The statistical models we rely on for data acquisition and evaluation have their own underlying uncertainties. Hence, we perform uncertainty quantification in this work to gain insights into the quality of screening of the solvents. The uncertainties are tabulated in Table . The relatively lower values of uncertainties stem from the fact that the neural network model in use has around 90% prediction accuracy. Therefore, we are highly confident of the results, and we believe that we have given an EDC screening tool to the scientific community, especially for chemicals where data isn't easily available. This work should be helpful to address the pressing concerns of safer chemistry in the making of photovoltaic materials.