The design of materials with targeted molecular properties, also called inverse molecular design, has been an enduring challenge in chemistry and material science. Several recent developments, including advances in computational resources and simulation throughput, the availability of large datasets amenable to machine learning (ML), and the proliferation of ML architectures for predicting various materials properties, have broadened the range of properties that are amenable to computational design. Nevertheless, a major capability gap still exists is predicting the stability of prospective materials with respect to specific stressors (e.g., thermal, chemical, voltaic, radiative, etc.) that might be encountered during use. It would represent a qualitative advance if stability, degradation, and aging behaviors of prospective chemicals and materials could be directly predicted during the design phase without experimental testing.