# Why I think AI might help me to do science

I’ll say a little about the type of research that I do, and how the traditional way that I solve research problems is limiting my ability to answer some of the questions I’m interested in.

My greatest research passion is developing and solving models of physical processes. Much of my work is about developing mathematical descriptions of how microstructure in blends emerges and evolves with time. Rather than attempt to describe this in words, perhaps a picture will help (if an interest in my blogs develops, I’ll upgrade and will be able to post the movie!). A snapshot of the phase separation process, according to one of my computational models. Anyone else see the resemblance to the Borg spaceship from Star Trek?

One frustration is that the processes I try to model are so complex that even the simplest theoretical description is unwieldy. This means that gaining physical insight from the models, which after all is the point of building models in the first place, becomes increasingly difficult. To illustrate, the above picture took two hours of computing time with a high end graphics card. Two hours might not seem like long but when you consider a model that has ten different independent variables, exploring how the predictions of the model changes as the values of the variable changes in a way that is statistically significant is just not possible. Without going into the details of statistical significance, let’s say that the minimum number of different values for any given independent variable is 5, which is optimistically low. If we only have one independent variable we need to do 5 runs of our computer model, which takes 10 hours. If we have two independent variables, we need to do 5 different variations of the second variable for each possible value of the first, so 25 runs or 50 hours. For three independent variables, this becomes 125 runs or 250 hours and so on. For 10 variables we are looking at 9765625 runs or over 200 years!

Now lets look a little bit more at what is happening inside my model. I divide space into discrete boxes (256 cubed in this case) and then I solve using finite differences my partial differential equations. The equations describe how the concentration of one of the polymers evolves in time based on some well established physics and then some assumptions particular to the circumstances I’m interest in, which might be how a surface affects the evolution. In each box, I’m solving the same equation but with different data. This means that if I have two boxes that look alike in terms of their concentration, and in my case, the concentration of their neighbours, I’m doing twice the work necessary. To some extent this is overcome by the use of parallel processing inside my graphics card, but even the best graphics cards have limitations on how many computations can be completed at once. For example, for 256 cubed, I have to solve for 16777216 boxes, but my card can only handle 2496 boxes at once, so that is still a lot of processing happening in serial.

What I started to appreciate, through conversations with some AI experts and listening to some fascinating developments at the somewhat cryptically entitled session Uncertainty Quantification in Multiscale Materials Modelling at the 2017 Fall MRS meeting, was that AI might be able to help out by learning the patterns. Rather than just solving the same problem on the same data, which is computationally intensive, my AI can say “I’ve seen that combination of inputs before, here is what is most likely to happen next”. It turns out that this is a lot quicker, but only once I’ve gone through a time consuming step of generating data to feed the AI so it can learn the trends. Well that’s the hope at least …