Significance Antifreeze proteins offer a technologically underutilized approach for controlling the freezing of water, a process intrinsically important in broad areas, such as medicine, agriculture, and food engineering, among others. To harness this capability, a better understanding of the measurable properties involved and their quantitative contribution to the observed antifreeze effect is needed. Here, we present a physically motivated method for the prediction of antifreeze activity purely from simulation, opening routes for the design of computationally optimized antifreeze materials.