  Tailoring materials to achieve a desired behavior in specific applications is
of significant scientific and industrial interest as design of materials is a
key driver to innovation. Overcoming the rather slow and expertise-bound
traditional forward approaches of trial and error, inverse design is attracting
substantial attention. Targeting a property, the design model proposes a
candidate structure with the desired property. This concept can be particularly
well applied to the field of architected materials as their structures can be
directly tuned. The bone-like spinodoid materials are a specific class of
architected materials. They are of considerable interest thanks to their
non-periodicity, smoothness, and low-dimensional statistical description.
Previous work successfully employed machine learning (ML) models for inverse
design. The amount of data necessary for most ML approaches poses a severe
obstacle for broader application, especially in the context of inelasticity.
That is why we propose an inverse-design approach based on Bayesian
optimization to operate in the small-data regime. Necessitating substantially
less data, a small initial data set is iteratively augmented by in silico
generated data until a structure with the targeted properties is found. The
application to the inverse design of spinodoid structures of desired elastic
properties demonstrates the framework's potential for paving the way for
advance in inverse design.
