Degradation is a technical and market hurdle in the development of novel photovoltaics and other energy devices. Understanding and addressing degradation requires complex, time-consuming measurements on multiple samples. To address this challenge, we present \textit{DeepDeg}, a machine learning model that combines deep learning, explainable machine learning, and physical modeling to: 1) forecast hundreds of hours of degradation, and 2) explain degradation in novel photovoltaics. Using a large and diverse dataset of over 785 stability tests of organic solar cells, totaling 230,000 measurement hours, DeepDeg is able to accurately predict degradation dynamics and explain the physiochemical factors driving them using few initial hours of degradation. We use cross-validation and a held-out dataset of over 9,000 hours of degradation of PCE10:OIDTBR to evaluate our model. We demonstrate that by using DeepDeg, degradation characterization and screening can be accelerated by 5-20x.