My conclusion reading this is that a gradient is a gradient is a gradient. If you can minimize one, you can minimize them all. The hard work would seem to be fi

My conclusion reading this is that a gradient is a gradient is a gradient. If you can minimize one, you can minimize them all. The hard work would seem to be figuring out how to transform into a gradient that your hardware can solve. It will also be interesting to see the kinds of systematic errors that will come as a result of the biases in the training set, and whether it can be used to predict what the structures would look like under slightly different conditions (e.g. pH).