Gradient Descriptions. The composition and gradient of a mobile phase in an LC method is highly relevant to the prediction of an arbitrary molecule's retention time. Our previous work has shown that for deep learning, it is important to balance providing models a sufficiently rich initial humanengineered description of the parameter space and not overdescribing the problem based on human intuition in a way that gives the model an inflexible and overly specific initialization. This is particularly true for mobile phase gradients which are described by complex, parameterized, piecewise time function that might contain extemporaneous information.