NON-TECHNICAL SUMMARY The objective of this project is to develop a new computational method to accelerate the design and discovery of advanced materials. Many materials with potential for transformative applications, such as clean energy and next-generation electronics, exhibit quantum properties that are too complex to simulate efficiently with existing tools, creating a bottleneck for scientific progress. This project introduces an approach that combines quantum theory with machine learning, inspired by the concept of data compression. Just as an image can be compressed by retaining only its most essential information, this method will reduce the complexity of quantum simulations by identifying and using a much smaller, representative set of quantum states. The resulting tool will enable accurate simulations of materials that were previously beyond reach. In particular, it will be applied to investigate a long-standing puzzle in the volume anomalies of heavy actinides, which may be resolved by simulating the complex interplay of spin–orbit coupling and crystal field effects, a mechanism that is currently poorly understood. Broader impacts will include training undergraduate- and graduate-student researchers, course development, and integration of the developed tools into a widely-used, publicly-available quantum-simulation toolkit. TECHNICAL SUMMARY This project aims to develop a new computational method to accelerate quantum embedding simulations for stro