  The true power of computational research typically can lay in either what it
accomplishes or what it enables others to accomplish. In this work, both
avenues are simultaneously embraced across several distinct efforts existing at
three general scales of abstractions of what a material is - atomistic,
physical, and design. At each, an efficient materials informatics
infrastructure is being built from the ground up based on (1) the fundamental
understanding of the underlying prior knowledge, including the data, (2)
deployment routes that take advantage of it, and (3) pathways to extend it in
an autonomous or semi-autonomous fashion, while heavily relying on artificial
intelligence (AI) to guide well-established DFT-based ab initio and
CALPHAD-based thermodynamic methods.
  The resulting multi-level discovery infrastructure is highly generalizable as
it focuses on encoding problems to solve them easily rather than looking for an
existing solution. To showcase it, this dissertation discusses the design of
multi-alloy functionally graded materials (FGMs) incorporating ultra-high
temperature refractory high entropy alloys (RHEAs) towards gas turbine and jet
engine efficiency increase reducing CO2 emissions, as well as hypersonic
vehicles. It leverages a new graph representation of underlying mathematical
space using a newly developed algorithm based on combinatorics, not subject to
many problems troubling the community. Underneath, property models and phase
relations are learned from optimized samplings of the largest and highest
quality dataset of HEA in the world, called ULTERA. At the atomistic level, a
data ecosystem optimized for machine learning (ML) from over 4.5 million
relaxed structures, called MPDD, is used to inform experimental observations
and improve thermodynamic models by providing stability data enabled by a new
efficient featurization framework.
