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

Abstract Three decades of intensive research in molecular nanomagnets have brought the magnetic memory in molecules from liquid helium to liquid nitrogen temperature. The enhancement of this operational temperature relies on a wise choice of the magnetic ion and the coordination environment. However, serendipity, oversimplified theories and chemical intuition have played the main role. In order to establish a powerful framework for statistically driven chemical design, we collected chemical and physical data for lanthanide-based nanomagnets to create a catalogue of over 1400 published experiments, developed an interactive dashboard (SIMDAVIS) to visualise the dataset, and applied inferential statistical analysis to it. We found that the effective energy barrier derived from the Arrhenius equation displays an excellent correlation with the magnetic memory, and that among all chemical families studied, only terbium bis-phthalocyaninato sandwiches and dysprosium metallocenes consistently present magnetic memory up to high temperature, but that there are some promising strategies for improvement.