Predictive Modeling of Phase Transitions in Functional Materials

This research uses large-scale computational screening to accelerate the discovery of new functional materials. By using high-throughput calculations, we can efficiently evaluate vast numbers of compounds to pinpoint those with optimal properties for specific applications, such as next-generation data storage and smart actuators. A key focus is predicting the finite-temperature properties of alloys and composites, particularly their solid-to-solid state phase transitions.

My work has centered on two important classes of materials:

Phase-Change Materials (PCMs): These materials can rapidly switch between crystalline and amorphous states, a property used in modern memory technologies. [cite_start]My research has used high-throughput methods to identify optimal PCM candidates.

Shape-Memory Alloys: These smart alloys can "remember" and return to a previous shape when heated. My postdoctoral work focused on predicting the complex phase transitions that govern this unique behavior.

To support this research, I also developed new computational methods. A key contribution is a first-principles approach to calculating the anisotropic coefficients of thermal expansion, which are crucial for understanding a material's stability and response to temperature changes. By combining these predictive models with experimental findings, we can gain new structural insights into complex systems, like the phase transitions within the Cu-Pd-Sn alloy system.