Abstract

Crystallographic defects play an important role to either enhancing desired properties of materials or to enable new functionalities not present in the ideal crystalline state. Point defects, for example, provide favourable electronic or optical properties of semiconductors or increase the strength of metallic alloys. Line defects, such as dislocations, are the origin for plastic deformation and play a decisive role for crystal growth phenomena. Grain boundaries control interfacial chemistry and are key for mechanical of metallic alloys or electronic properties of gas sensing materials. 

Introduction of a defect into a perfect crystal creates a challenge for ab initio modelling due to increase in the size of the system with lowered symmetry. This is where a combination of traditional methods with machine learning (ML) can make an impact. This mini-colloquium is devoted to achievements in using ab-initio and/or ML approaches for studying physical properties of crystallographic defects and their effect on material behavior. The investigated systems can vary from metals and semiconductors to 2D materials. Of particular relevance are electronic properties of point defects, e.g. dopants in semiconductors, including spin centers that can be used in quantum technologies. The symposium also welcomes submissions regarding correlation of defects with mechanical phenomena such as solute-solution strengthening or grain boundary segregation.

Invited speakers

to be announced

Organizers