Two-Dimentional (2D) Materials
Twistronics: The recent discovery of unconventional Mott-like and superconducting phases in twisted bilayer graphene rotated at the magic angle has inspired experimental and theoretical interest in van der Waals heterostructure systems. These vertical heterostructures involve stacks of various two-dimensional layers. It is crucial to develop a systematic and efficient numerical approach to modeling these systems, in order to navigate the extensive parameter space it can give. However, the computations are challenging due to the system size. In our group, we developed an ab initio multi-scale numerical scheme to simulate these systems, which is based on Wannier transformations on top of the density functional theory calculations. We have applied these methods to study the electronic and mechanical properties of several heterostructures.
Magnetism in low-dimensions: The recent discovery of single-layer magnetic materials has exciting prospects for adding tailored functionality to nanostructured devices. However, the interplay between magnetism and reduced dimensionality is not fully understood, and requires careful collaboration between theoretical and experimental studies in order to both understand and control the magnetic properties.
Van der Waals heterostructures: One method of functionalizing nanostructures is by introducing intercalants, such as lithium, between the layers. In addition to direct applications in battery technology, the presence of intercalants can modify the electronic properties through structural changes and electron doping.
Machine Learning for Materials
The entire space of materials - including organic and inorganic crystal structures - is huge. Insight into the behavior of materials can be gained by using data-driven tools to reveal hidden physical relationships which are embedded in a high-dimensional feature space, not readily perceived without statistical tools. The goal of this research is multifaceted and includes both knowledge discovery - gaining new physical insight through data analytics methods - and materials discovery. The Kaxiras group is currently exploiting machine learning tools to explore two-dimensional magnetic materials, catalysis on metal surfaces and branched electronic flow in graphene.
Methodology for Electronic Excitations
Using time-dependent density functional theory (TDDFT), the dynamics of quantum systems can be explored and leveraged to make nanoscale devices. Excitations, optical absorption, and chemical reactions are all time-dependent processes which can be analyzed with the code developed in this group. Recently, we have explored transforming the time-dependent Kohn-Sham equations to imaginary time, allowing for novel convergence techniques for finding stationary states, and enabling the use of non-Born-Oppenheimer imaginary-time path integrals to study the complicated nuclear-electronic coupling in systems undergoing zero-point motion.
We explore the thermodynamic and kinetic processes involved in heterogeneous catalysis using density functional theory (DFT) calculations and micro-kinetic modeling. By mapping out the reaction mechanisms of catalysts, we can design materials with enhanced catalytic performance. We study various catalytic systems, which include transition metals, metal oxides, and 2D materials. Flat and stepped surfaces, along with nanoparticles, are model structures used for the exploration. We are interested in several reactions types: alcohol dehydrogenation, cross-coupling, displacement, etc. Our recent studies focus on the structural restructuring of metal surface alloys upon molecular adsorption and the pre-exponential factor modeling of alcohol dehydrogenations.