Metal-Organic Frameworks (Custom-build 2D Materials)
Two-dimensional metal-organic frameworks (2D-MOFs) are highly versatile material system which can be custom-build for the next generation technologies such as organic topological insulators for dissipationless electronics and 2D magnetic materials. One of the key goals of this project is to investigate long-range magnetic ordering in 2D-MOF structures. Elucidating such magnetic properties can be important for understanding possible nontrivial topological electronic properties of these systems, since the latter can result from magnetic phenomena but also be the cause of well-defined spin structure. The presence of such spin structure in 2D materials provides an ideal platform for the realisation of 2D magnets, with applications including in sensing and hard-disk data storage. 2D-MOF structures can be engineered to implement 2D magnets by the selection of metal ions and the selection of structural morphologies which is the subject of our ongoing research.
Due to the availability of a large number of building blocks (molecules and metal atoms) as well as several possibilities to assemble 2D materials by bottom-up approach, the 2D-MOFs form a tremendously large design space which is impractical to explore via direct quantum mechanical simulations based on DFT theory. Alternatively, a machine learning framework, carefully trained by carrying out the rigorous theoretical analysis of a set of selected materials, can rapidly screen a large database of unknown materials to reliably identify those of the same character at only a fraction of the computational cost. In this project, our team is building a machine learning framework which will utilize advanced techniques such as quantitative structure-property relationship (QSPR) to establish a correlation between the electronic and spin properties of 2D-MOFs with their composition, structure, and symmetry. To enable accurate and fast learning, the QSPR analysis will be conducted by employing a variety of advanced algorithms such as linear regression analysis, decision tree regression, and non-linear support vector machines. The detailed insights obtained by QSPR will train an artificial neural network for high-throughput screening and computer-aided design of 2D-MOF TIs.