Machine learning accelerated topological design of metal-organic frameworks

金属有机磁体（MOMs）是金属有机框架（MOFs）的一个子分类，其在由有机连接体桥接的金属中心之间具有强的磁性相互作用。MOM因其潜在的高电导率、强磁相互作用和低维度而被视为新量子技术的基石。这些框架磁体由于其金属和有机组分的可调谐性而允许在设计上比其常规无机对应物更灵活。接下来的挑战是理解MOMs中的磁相互作用与连接器、金属中心和拓扑结构的巨大空间之间的联系。新材料的实验发现是缓慢的，困难的，昂贵的。在任何新材料可以被表征和分类之前，合成路线必须被艰苦地发现。基于密度泛函理论（DFT）的第一性原理计算已被证明可以加速发现过程：计算可以筛选出数千种最有前途的材料，甚至在昂贵的实验合成之前就可以对新材料进行试验和测试。由于可能的建筑单元数量巨大，新材料的探索空间广阔，远远大于世纪对无机晶体的研究。在这些复杂的情况下，即使DFT筛选也可能过于昂贵，因此我们已经展示了如何使用DFT生成的机器学习势（MLP）进行预筛选。在这个项目中，我们将开发一个组合的MLP+DFT方法，并发现新的MOMs。这些将使用计算机模拟方法进行表征，以向我们的实验同事提供路标。

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.