Abstract
The Organic Materials Database (OMDB) is an open database hosting about 22,000 electronic band structures, density of states and other properties for stable and previously synthesized 3-dimensional organic crystals. The web interface of the OMDB offers various search tools for the identification of novel functional materials such as band structure pattern matching and density of states similarity search. In this work the OMDB is extended to include magnetic excitation properties. For inelastic neutron scattering we focus on the dynamical structure factor $S(\mathbf{q},\omega)$ which contains information on the excitation modes of the material. We introduce a new dataset containing atomic magnetic moments and Heisenberg exchange parameters for which we calculate the spin wave spectra and dynamic structure factor with linear spin wave theory and atomistic spin dynamics. We thus develop the materials informatics tools to identify novel functional organic and metalorganic magnets.
Highlights
Magnetism and magnetically ordered materials have played a crucial role in the development of the technology used in our everyday life
Local magnetic moments in organic materials can arise due to transition metal and rare earth ions embedded in the molecules or due to local unsaturized bonds as they occur in stable organic radicals [12,13,14]
We report about the implementation of a novel dataset for organic magnets into the organic materials database Organic Materials Database (OMDB) [19,37]
Summary
Magnetism and magnetically ordered materials have played a crucial role in the development of the technology used in our everyday life. The data were calculated by means of ab initio calculations and comprises information about local magnetic moments, magnetic exchange coupling, expected magnetic ground state, as well as spin wave excitation spectra and the dynamic structure factor S(q, ω) for hundreds of previously synthesized organic molecular crystals and metal organic frameworks. These data are embedded into the existing framework of the OMDB and can be explored using interactive statistics and nontrivial search tools such as pattern matching [38,39].
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