Intelligent, autonomous parameter space exploration of self-assembly simulations

Researchers studying self-assembly are often plagued with a problem: the systems they study exhibit emergent behavior that could result from any combination of the many independent parameters they can tune. The typical solution to this problem is to restrict a study to the few most interesting variables and perform a screening experiment on a grid in this space. But what can be done when it is not clear which variables are most important? The scale of these studies quickly gets out of hand as analysis, visualization, and even selecting new experiments are multiplied by the dimensionality of the parameter space. Here we discuss approaches to incorporate machine learning methods into the experimental design and analysis loop of exploratory self-assembly simulations in order to optimize computational time spent simulating interesting and novel behaviors. By harnessing these methods, we can begin to probe the behavior of even more complex design spaces.