ENGINEERING TECHNOLOGY

Reveal the structural similarities of transition group metal ions through big data


Understanding the structure-activity relationship between the “structure-property” of compounds from the atomic scale is a basic problem in the field of material science, and clarifying the statistical characteristics of the micro-local structure of matter helps scientists to better explain scientific problems in physics, materials, chemistry and other disciplines. Today, the widely used basic data of material science are mostly derived from the mid-to-late 20th century, and the values are relatively old. For example, the value of the ion radius that has been widely used for nearly half a century in the field of material science is derived from the statistics of the 1960s, and with the accumulation of material science data, massive data will bring new values and new cognition to the field of material science.

Recently, Meng Sheng, researcher of the State Key Laboratory of Surface Physics of the Institute of Physics of the Chinese Academy of Sciences/Beijing National Research Center for Condensed Matter Physics, and Liu Miao, a distinguished researcher, collaborated with Kristin A. Persson, professor of Lawrence Berkeley National Laboratory in the United States, to systematically analyze the crystal information of more than 30,000 transition metal oxides, and extract the coordination structure, valence state, ion bond length, atomic magnetic moment, and local deformation caused by the Jahn-Teller effect. Detailed information on physical quantities, such as structural stability, replaces the traditional practice of characterizing materials with only one parameter, “ion radius”, which has been widely used for nearly half a century.

This work shows that each transition group metal ion has its own “shape”, “size” and “atomic magnetic moment”, thus outlining the “personality” of each transition metal ion in the solid; Through artificial intelligence methods, structural similarity “templates” of transition metal ions are further obtained, which can be used to guide the rapid evaluation of new material design and stability.

Based on the knowledge of the structural similarity of the above ions, the team expanded the space of humanly known compounds with the help of ion substitution, generated more than 60,000 new structures unknown to humans, and found 5,000 stable new compounds (Ehull < 20meV/atom) through high-throughput calculations on these structures, which are easy to be tested and synthesized, effectively expanding the compound “territory” of materials science. The study proposes new ideas for exploring the broader phase space of inorganic materials, and all compound information is available in the Atomly Materials Database (https://atomly.net/).

Figure 1: Coordination preference of transition metal ions in a local environment (“shape” of the ions)

Figure 2.3d the relationship between the length of the TM-O bond and the valence state and thermodynamic stability

Figure 3.3d statistical results between the atomic magnetic moment and valence state of transition metal ions and the Jahn-Teller effect

Figure 4.Local structural similarity of transition metal ions. Dark colors represent high similarity

The research results were published in Advanced Science under the title Ofsona of Transition Metal Ions in Solids: A Statistical Learning on Local Structures of Transition Metal Oxides. The research work is supported by the National Key Research and Development Program and the Chinese Academy of Sciences. (Source: Institute of Physics, Chinese Academy of Sciences)

Related paper information:https://doi.org/10.1002/advs.202202756

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