Data mining, dashboards and statistics: a powerful framework for the chemical design of molecular nanomagnets

Two decades of intensive research in lanthanide-based molecular nanomagnets have brought the magnetic memory in molecules from liquid helium to liquid nitrogen temperature. In the pursuit of new derivatives with improved operational temperatures, several "rational" strategies have been proposed and applied through a fluid transfer of knowledge between theoreticians and experimentalists. These have mainly focused on the choice of the magnetic ion and the design of an adequate coordination environment, both in terms of magnetic anisotropy and molecular vibrations. However, much of the progress has been achieved by serendipity, oversimplified theories and chemical intuition. In order to draw conclusions on the chemical design key parameters that govern the physical behavior of molecular nanomagnets in terms of magnetic memory, we apply here a state-of-the-art inferential statistical analysis to a body of over a thousand published experiments. Our analysis shows that the effective barrier derived from an Arrhenius equation displays an excellent correlation with the magnetic memory, and that there are only two promising strategies between all alternatives proposed so far, namely terbium bis-phthalocyaninato sandwiches and dysprosium metallocenes. In addition, we provide an interactive dashboard for visualizing the collected data, which contains all the reported cases between 2003 and 2019. This meta-study aims to dispel widespread theoretical misconceptions and will allow researchers in the field to avoid experimental blind alleys.