Sep 2025 – Jan 2026 · 4 mos

• Working in the Jornada group on computational methods to discover new materials for nanoelectronic devices as a continuation og the key topics in my Ph.D. dissertation

May 2020 – Present · 6 yrs 1 mo

• Dissertation Topic: Computational discovery of materials for nanoscale electronic device applications
• Advisors: Prof. Felipe H. da Jornada, Prof. Evan Reed
• The rapid miniaturization of electronic devices, down to the nanometer scale, has been instrumental in facilitating the increased computing power of our electronic devices. As we approach the fundamental physical bottlenecks of conventional materials, such as silicon and copper, that have underpinned the progress over the past decade, we seek material alternatives that can deliver better performance at the nanometer (nm) or even angstrom (Å) scale. Experimentally synthesizing and validating even a single alternative material candidate is a time-consuming and involved process. In addition, identifying a new candidate material from the vast number of possible compounds to replace the conventional ones requires simultaneously optimizing multiple desirable material properties. Computational methods, therefore, are uniquely well-positioned to parse through this large space of materials and identify promising alternative material candidates.
• In my work, we showed that, using database-accessible surrogates for complex material properties, one can efficiently screen 15,000+ metals to identify ~10 materials predicted to outperform Cu and currently proposed alternatives, such as Ru. We also showed how one can leverage the chiral structure of Te-based one-dimensional van der Waals wires at the atomic scale to obtain an inductor with high-inductance density, offering an alternative to traditional Cu inductors based on macroscopic coils. We also developed a machine-learning interatomic potential approach for efficiently and accurately studying layered materials, including twisted bilayers that display moiré patterns. This approach allows the rapid prediction of the structures of various two-dimensional (2D) heterostructures as alternative materials, leading to new emergent properties for future devices.