Machine learning accelerated topological design of metal-organic frameworks

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Metal-organic magnets (MOMs) are a subclassification of metal-organic frameworks (MOFs) that have strong magnetic interactions between metal centres bridged by organic linkers. MOMs are targeted as the building blocks of new quantum technology for their potential high electrical conductivity, strong magnetic interactions, and low dimensionality. These framework magnets allow for more flexibility in design over their conventional inorganic counterparts due to the tunability of both their metallic and organic components. The challenge then is understanding the connection between the magnetic interactions in MOMs and the vast space of linkers, metals centres, and topologies. The experimental discovery of new materials is slow, difficult, and expensive. Synthesis routes must be laboriously discovered before any new material can be characterised and categorised for its utility. First principles calculations based on density-functional theory (DFT) have been shown to accelerate the discovery process: calculations can screen thousands of candidates for the most promising materials, allowing new materials to be tried-and-tested even before the costly experimental synthesis set takes place. Due to the enormous number of possible building units, the search space of new MOM materials is vast, far greater than the 20th century's investigation of inorganic crystals. In these complex cases, even DFT screening can be too costly, so we have shown how to pre-screen using a DFT-generated machine-learned potential (MLP). In this project we will develop a combined MLP+DFT approach and discover new MOMs. These will be characterised using in silico methods to signpost to our experimental colleagues.