  Informatics-driven approaches, such as machine learning and sequential
experimental design, have shown the potential to drastically impact
next-generation materials discovery and design. In this perspective, we present
a few guiding principles for applying informatics-based methods towards the
design of novel nuclear waste forms. We advocate for adopting a system design
approach, and describe the effective usage of data-driven methods in every
stage of such a design process. We demonstrate how this approach can optimally
leverage physics-based simulations, machine learning surrogates, and
experimental synthesis and characterization, within a feedback-driven
closed-loop sequential learning framework. We discuss the importance of
incorporating domain knowledge into the representation of materials, the
construction and curation of datasets, the development of predictive property
models, and the design and execution of experiments. We illustrate the
application of this approach by successfully designing and validating Na- and
Nd-containing phosphate-based ceramic waste forms. Finally, we discuss open
challenges in such informatics-driven workflows and present an outlook for
their widespread application for the cleanup of nuclear wastes.
