Systematic searches for new inorganic materials assisted by materials informatics

Abstract

We introduce our proprietary Materials Informatics (MI) technologies and our chemistry-oriented methodology for exploring new inorganic functional materials. Using machine learning on crystal structure databases, we developed ‘Element Reactivity Maps’ that displays the presence or the predicted formation probability of compounds for combinations of 80 × 80 × 80 elements. By analysing atomic coordinates with Delaunay tetrahedral decomposition, we established the concept of Delaunay Chemistry. This enabled us to design crystal structures by combining Delaunay tetrahedra of known compounds and to develop the ‘Crystal Cluster Simulator’ web system. We also developed the Starrydata2 web system to collect large-scale experimental data on material properties from plot images in academic papers. This dataset supported us to select candidate materials for new thermoelectric materials through various data analyses. In large-scale synthesis experiments involving over 7,000 samples, we discovered numerous new phases, including solid solutions of known structures in new combinations of elements. Using sodium metal in synthesis and our proprietary ion diffusion control technologies, we discovered new cage-like compounds by extracting monovalent cations from materials with nano-framework structures, as well as new intercalation compounds. The Element Reactivity Maps were also used to select barrier metals for device electrodes, and an autonomous contact resistance measurement system is under development.

Keywords

• Inorganic chemistry
• materials informatics
• machine learning
• crystal structure analysis
• database
• high-throughput synthesis