Machine learning concepts for inverse materials design

In order for artificial neural networks to learn a task, one must solve an inverse design problem. What are all the node weights for the network that will give the desired output? The method by which this problem is solved by computer scientists can be harnessed to solve inverse design problems in soft matter. I will discuss how we have used such approaches to design mechanical and flow networks that can perform functions inspired by biology. I will also show how we can exploit physics to go beyond artificial neural networks by using local rules rather than global gradient descent approaches to learn in a distributed way.