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.

 

Relevant for magnetic research, in the last decade there has been a significant progress in the use of spin-polarized STM (SP-STM) for the imaging and manipulation of complex magnetic textures, like frustrated antiferromagnets, spin spirals, domain walls, skyrmions with topological properties, etc. The magnetic ground state, electronic, and dynamic properties of such surface systems can be obtained by using computational methods, for instance based on multiscale modeling employing density functional theory (DFT), spin models, and spin dynamics. The comparison of the magnetic structures and their time evolution with available experiments in the forefront of magnetic research can be obtained by performing SP-STM simulations.

 

The proposed theoretical PhD research covers analytical, programming, and numerical tasks in the fields of complex surface magnetic textures, SP-STM/STS and vector spin transport in high spatial and energy resolution. One of the goals is to develop the combined theory of various magnetoresistance (MR) effects, such as tunneling MR (TMR), tunneling anisotropic MR (TAMR) and non-collinear MR (NCMR), and implement them into the 3D-WKB-STM code to extend the STM imaging capability of magnetic surfaces. Another goal is to develop and implement the theory of spin transfer torquance amd longitudinal spin conductance spectroscopy into the 3D-WKB-STM code, and new vector spin transport theories into more complex electron tunneling models. With the newly developed computational methods and by using suitable machine learning approaches based on a large amount of simulated SP-STM/STS data, we solve emerging technologically relevant problems in surface magnetism. Our scientific goal is to contribute to the deep understanding of electron charge and spin transport phenomena that can lead to future technological exploitations in magnetic data storage and information processing.

 

During the PhD training, the successful applicant will gain considerable research skills in multiscale materials modeling, from first principles DFT to electron transport and spin dynamics 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