Within the project, we have shown how important chemical heuristics (intuitive strategies within chemistry) and design principles based on coordination environments and bonding analysis are to understand, find and design new materials. Within an opinion, we have pointed out, how easily old chemical heuristics can nowadays be tested, and completely new ones can be developed based on tools from data analysis and machine learning (Trends Chem. 2021, 3, 86).
We have assessed the predictive power of a famous chemical heuristic, the Pauling rules, which connects the structure of the material to the stability, for the first time (Angew. Chem. Int. Ed. 2020, 59, 7569). We have seen that they are only of limited predictive power and they only work well for less than 13% of all tested oxides. Since they are a cornerstone of solid-state chemistry, this was also a very important result for solid-state chemistry, crystallography, and the understanding of crystalline materials. To do so, we have developed automatic tools to determine coordination environments based on crystal structure data (Acta Cryst B 2020, 76, 683).
We have developed automatic tools to perform bonding analysis for crystalline structure based on electronic structure theory calculations. This contributes to the chemical understanding of materials and will simplify the development of new chemical heuristics based on bonding analysis tools. We have already used these tools to test a new implementation in the program Lobster (J. Comput. Chem 2020, 41, 1931) and to understand and rationalize results from ab intio high-throughput searches for new materials that can be used as ferroelectric materials or in photovoltaics (Proc Natl Acad Sci USA 2021, 118, e2026020118). The tools for automated bonding analysis have already been disseminated and a publication in a peer-reviewed journal is planned.
We have developed a new design principle for thermoelectric materials that is based on a new theory on amorphous-like heat conduction based on the well-known thermoelectric material (Mater. Today Phys. 2021, 100344)
To speed up the search for new thermoelectric materials, we have also tested machine-learned interatomic potentials (J. Chem. Phys. 2020, 153, 044104). We have developed new strategies to build databases to learn these potentials. We have arrived at very good agreement of the computed phonon properties based on the potentials with ab initio reference data for several silicon allotropes. We have therefore shown that transferable interatomic potentials can be developed and used to compute phonon properties. We have also shown that thermal conductivities that are very relevant for thermoelectric materials can be computed with these potentials as well.
