Inverse design, where we can prescribe the materials attributes based on a desired target property label, is a singular ambition of data-driven material design, and if successful will finally enable market-pull innovation as opposed to conventional technology-push. This is extremely challenging however, since there could be numerous combinations of materials characteristics that present the same properties and predicting a traditional “structure/property relationship” does not distinguish between them. It is even more challenge in nanomaterials design since the design space is even greater, and inverse property/structure relationships will typically need to encompass multi-functionality. Recently a solution to this problem has been devised based on multi-target machine learning that draws upon this multi-functionality and predicts the exact nanoparticle structure that will deliver a set of desirable properties simultaneously, with a fault tolerance. The method focusses the outcome on the most important characteristics in an entirely data-driven way, and with comparable accuracy and generalizability as traditional forward structure/property machine learning predictions. This approach works very well for ordered materials, but its efficacy for highly disordered and complex non-crystalline structures, such as metal nanoparticles using for catalysis, is unknown. In this project you will apply and test this multi-target inverse design method to data describing metal nanoparticle catalysts and develop a model to inform experimental and manufacturing strategies. The results can be made actionable using statistical leaning such as Bayesian networks. The data set will be provided.