The 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. STM 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, topologically protected skyrmions, 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, and spin dynamics. One of the main goals is to bridge the recently developed electron vector spin transport capability of SP-STM with atomistic spin dynamics in order to gain microscopic insights into the creation and annihilation of topological magnetic objects by local torques generated by the tunneling current of the STM tip. Another goal is to develop and implement vector spin transport theories in more complex electron tunneling models, and solve emerging technologically relevant problems with the newly developed computational methods. 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
Motivation for theoretical and computational work, programming skills, good communication skills, open personality, good command of quantum mechanics and solid state physics