  In materials science, the challenge of rapid prototyping materials with
desired properties often involves extensive experimentation to find suitable
microstructures. Additionally, finding microstructures for given properties is
typically an ill-posed problem where multiple solutions may exist. Using
generative machine learning models can be a viable solution which also reduces
the computational cost. This comes with new challenges because, e.g., a
continuous property variable as conditioning input to the model is required. We
investigate the shortcomings of an existing method and compare this to a novel
embedding strategy for generative models that is based on the binary
representation of floating point numbers. This eliminates the need for
normalization, preserves information, and creates a versatile embedding space
for conditioning the generative model. This technique can be applied to
condition a network on any number, to provide fine control over generated
microstructure images, thereby contributing to accelerated materials design.

Machine Learning;Materials Science