UC San Diego
First-Principles Studies of Surface Energies of Magnetic Full-Heuslers and Machine Learning of Hybrid Perovskites
- Author(s): Wong, Joseph
- Advisor(s): Yang, Kesong
- et al.
Materials design is a cornerstone of every device. Historically, the materials selection process was characterized by a time consuming, expensive, Edisonian approach. In recent years however, rapid advancements in computational power and materials simulation software has spawned the field of computational materials science. Computational materials science opens a new avenue to materials discovery called high-throughput materials design. This approach allows for rapid prototyping of materials in a large, complex chemical space. In this work, the scope of highthroughput materials design approach is used in the analysis of several topics: magnetic full-heuslers, hybrid perovskites, and grain boundary structures. Using high-throughput density functional theory (DFT), we study the surface energy of 68 magnetic full heuslers to guide the synthesis of magnetic tunnel junctions for applications in memory storage devices. We employ a high-throughput machine learning approach to explore the chemical space of single and double perovskite materials for applications in stable, high-performance solar cells. We also look deeper into hybrid perovskite materials in a literature review of two-dimensional hybrid perovskites, which demonstrate greater stability and tunable band gaps with simple fabrication routes. In addition, their strong binding energies lead to strong light emitting properties, with potential applications in light emitting diode devices. We also examine the configurational entropy of yttria-stabilized zirconia grain boundaries and provide example usage and applications of AIMSGB, an open-source python library for grain boundary structure generation.