This research will accelerate new understanding of the interplay of cation complexity on phase stability of high entropy rare earth silicates in extreme environments. The computation-experiment-feedback loop coupled with machine learning and high throughput computation will result in heretofore unrealized linkages of entropy-induced material stability, thermal properties, and corrosion resistance. The project will result in advances in fundamental understanding and discovery of novel materials that can be designed for specific extreme environment applications. The computational approach to materials discovery will utilize AFLOW: high throughput property prediction. These predictions will be tested by characterizing rare earth silicates synthesized via solid state sintering, chemical techniques for improved cation mixing, and gas phase pulsed laser deposition of thin films.  Phase stability and chemical disorder will be characterized through use of techniques including X-ray diffraction and transmission electron microscopy. Resulting stability of rare earth silicate mixtures will inform improvements in the computational approach for materials discovery. Additionally, computational approaches will be used to predict phonon transport and thermal properties. These predicted thermal properties will be compared against thermal conductivity measurements as a function of temperature through use of time domain and steady state thermoreflectance, and hot disk techniques. Environmental stability will be experimentally characterized using "steam-jet" testing, an extreme environment laboratory test creating high-temperature, high-velocity, reactive steam representative of the combustion environment. Results from both the thermal and environmental testing will be used to validate and advance the computational approaches and property-based materials discovery.