Scanning tunneling microscopy (STM) is one of the most useful local probe methods for studying physical and chemical phenomena at surfaces of various materials in atomic resolution by using tunneling electrons. Its energy-resolved mode, scanning tunneling spectroscopy (STS), provides unprecedented information on the local electronic structure of novel material surfaces. The STM equipment can not only image but also manipulate surface structures with atomic precision. The development of the STM, awarded by the Nobel prize in Physics in 1986, contributed to the emergence of contemporary nanoscience and nanotechnology.

 

Materials with reduced dimensions are known to have different properties compared to their bulk counterparts. Two-dimensional (2D) materials, like graphene among many others, and layered heterostructures show intriguing properties that are promising for using them as building elements of devices in a variety of future technologies in the wide fields ranging from sensorics to catalysis, solar cells, molecular electronics/spintronics, nano-biomedicine, superconductivity, etc. Computational methods, for instance multiscale modeling employing geometric models, density functional theory (DFT), Monte Carlo methods, and STM simulations are ultimately useful for understanding the physical and chemical properties of such novel materials and material combinations.

 

The proposed theoretical PhD research covers analytical, programming, and numerical tasks in the fields of 2D materials research, twistronics and STM/STS simulations. The main goal is to further develop the recently published Moiré Plane Wave Expansion (MPWEM) model (https://doi.org/10.1103/PhysRevB.110.195418), which is capable of simulating STM images of generally incommensurate 2D material bilayers, by encoding electronic structure information into the geometric model, and allow for a high energy-resolution STS capacity. Another goal is to develop a 3D imaging function of MPWEM, which would enable the simulation of STM/STS experiments within a wide range of tunneling parameters. Verification via DFT-based STM/STS simulations will be required for computationally feasible cases. Using the new methodological developments of the few-parametered MPWEM in combination with predictive capabilities of suitable machine learning approaches, we solve emerging technologically relevant problems in 2D materials research. These theoretical research activities, partly in close combination with experiments, will contribute to an in-depth atomic level understanding of the structural and electronic properties of a variety of novel 2D material heterostructures, which lays the basis for future technological exploitations.

 

During the PhD training, the successful applicant will gain considerable research skills in multiscale materials modeling, from geometric models to first principles DFT and electron transport methods. Moreover, soft skills in programming, problem solving, analytical thinking, teamwork, and international collaborations will be developed. The work is in part in collaboration with international research groups, and includes regular visits at foreign partners and at international conferences.

 

More details on the supervisor: http://www.phy.bme.hu/~palotas/index.html