Molecular nanomagnets are tiny physical systems capable of presenting magnetic memory in a single molecule. Their quantum behaviour and their infinite configurability, among other factors, make them useful for fundamental studies, but also for potential applications in the field of quantum technologies.
Although data science has already broken ground in chemical research and the design of new materials, in the case of molecular nanomagnets, chance and chemical intuition continue to play the main role.
In order to establish a powerful framework for statistical-based chemical design, the University of Valencia team has just published an article in Nature Communications that compiles around 20,000 chemical and physical data on lanthanide-based nanomagnets – chemical elements with interesting magnetic and optical properties –; catalogues more than 1,400 experiments published between 2003 and 2019, and develops an interactive panel – SIMDAVIS (Single Ion Magnet DAta VISualisation) – for visualising and processing the collected data. It is a big data tool aimed at the chemical design of nanomagnets and magnetic nanostructures, a field of interest for the future of computing, electronics, quantum devices or biomedicine, among others.
“Putting the data within the reach of the scientific community makes it possible to understand the research results as a whole, broaden the scientific perspective and provide more precise conclusions”, says Alejandro Gaita-Ariño, CIDEGENT researcher at the ICMol and head of the project. “Analysing both the positive and negative results allows us to better understand the materials studied and provides information to refine existing theories and develop new ones”, adds the scientist.
The Gaita-Ariño group at the Institute of Molecular Science focuses its research on the field of molecular magnetism and quantum computing, within the framework of the Molecular Materials Research Unit. In addition to the ICMol team, the Oak Ridge National Laboratory (USA) has participated in the study. The work has had the collaboration of the statistics section of the Central Support Service for Experimental Research (SCSIE) of the University of Valencia.
Read the original article on University of Valencia.
