Incomplete conditional density estimation for fast materials discovery

Designing new physical products and processes requires enormous experimentation. The scientific simulators play a fundamental role for such design tasks. To design a new product with certain target characteristics, a search is performed in the design space by trying out a large number of design combinations through simulators before reaching to the target characteristics. However, searching for the target design using simulators is generally expensive and becomes prohibitive when the target is either revised or only partially specified. To address this problem, we use a machine learning model to predict the design in single step using the target product specifications as input. We overcome two technical challenges: the first caused due to one-to-many mapping when learning the inverse problem and the second caused due to a user specifying the target specifications only partially. We unify a conditional variational auto-encoder model (to address the partial target specification) with mixture density networks (to address the one-to-many mapping) and train an end-to-end model to predict the optimum design.