First-Principles Studies of Surface Energies of Magnetic Full-Heuslers and Machine Learning of Hybrid Perovskites

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