  Inverse material design is a cornerstone challenge in materials science, with
significant applications across many industries. Traditional approaches that
invert the structure-property (SP) linkage to identify microstructures with
targeted properties often overlook the feasibility of production processes,
leading to microstructures that may not be manufacturable. Achieving both
desired properties and a realizable manufacturing procedure necessitates
inverting the entire Process-Structure-Property (PSP) chain. However, this task
is fraught with challenges, including stochasticity along the whole modeling
chain, the high dimensionality of microstructures and process parameters, and
the inherent ill-posedness of the inverse problem. This paper proposes a novel
framework, named PSP-GEN, for the goal-oriented material design that
effectively addresses these challenges by modeling the entire PSP chain with a
deep generative model. It employs two sets of continuous, microstructure- and
property-aware, latent variables, the first of which provides a
lower-dimensional representation that captures the stochastic aspects of
microstructure generation, while the second is a direct link to processing
parameters. This structured, low-dimensional embedding not only simplifies the
handling of high-dimensional microstructure data but also facilitates the
application of gradient-based optimization techniques. The effectiveness and
efficiency of this method are demonstrated in the inverse design of two-phase
materials, where the objective is to design microstructures with target
effective permeability. We compare state-of-the-art alternatives in challenging
settings involving limited training data, target property regions for which no
training data is available, and design tasks where the process parameters and
microstructures have high-dimensional representations.
