  Functional soft materials, comprising colloidal and molecular building blocks
that self-organize into complex structures as a result of their tunable
interactions, enable a wide array of technological applications. Inverse
methods provide systematic means for navigating their inherently
high-dimensional design spaces to create materials with targeted properties.
While multiple physically motivated inverse strategies have been successfully
implemented in silico, their translation to guiding experimental materials
discovery has thus far been limited to a handful of proof-of-concept studies.
In this Perspective, we discuss recent advances in inverse methods for design
of soft materials that address two challenges: (1) methodological limitations
that prevent such approaches from satisfying design constraints and (2)
computational challenges that limit the size and complexity of systems that can
be addressed. Strategies that leverage machine learning have proven
particularly effective, including methods to discover order parameters that
characterize complex structural motifs and schemes to efficiently compute
macroscopic properties from the underlying structure. We also highlight
promising opportunities to improve the experimental realizability of materials
designed computationally, including discovery of materials with functionality
at multiple thermodynamic states, design of externally directed assembly
protocols that are simple to implement in experiments, and strategies to
improve the accuracy and computational efficiency of experimentally relevant
models.
