Abstract
Inverse materials design can be viewed as a reverse engineering process of material constituents from a user given set of targets characterizing material properties. Machine learning techniques could be used to map a feature space x of material properties to a contextual latent space z over an object space O , and then reconstruct a distinct object space O^{\prime} over the latent space z to maximize the context c . In this paper, we introduce a new machine learning tool, called MatFlow, for inverse material design using machine learning. In MatFlow, we first learn the latent space z characterizing a context feature set c . MatFlow then helps identify novel materials O^{\prime} over the latent space z with a potential to exceed the contextual threshold \theta without destabilizing the latent space. We explain MatFlow features and its capabilities using an application in quantum dye material discovery. The main focus of this paper is developing a computational strategy to identify z in the context of c , and inform the characteristics of z space values of O′ most likely to meet the contextual threshold θ .