Toward Knowledge Engineering Using MatFlow for Inverse Quantum Dye Design

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
$O^{\prime}$
most likely to meet the contextual threshold
$\theta$
.