TY - CONF A1 - George, Janine T1 - Data-driven chemical understanding with geometrical and quantum-chemical bonding analysis N2 - Chemical bonding and coordination environments are crucial descriptors of material properties. They have previously been applied to creating chemical design guidelines and chemical heuristics. They are currently being used as features in machine learning more and more frequently. I will discuss implementations and algorithms (ChemEnv and LobsterEnv) for identifying these coordination environments based on geometrical characteristics and chemical bond quantum chemical analysis. I'll demonstrate how these techniques helped in testing chemical heuristics like the Pauling rule and thereby improved our understanding of chemistry. I'll also show how these tools can be used to create new design guidelines and a new understanding of chemistry. To use quantum-chemical bonding analysis on a large-scale and for machine-learning approaches, fully automatic workflows and analysis tools have been developed. After presenting the capabilities of these tools, I will also point out how these developments relate to the general trend towards automation in the field of density functional based materials science. T2 - ICAMS Interdisciplinary Centre for Advanced Materials Simulation Seminar Series CY - Online meeting DA - 24.11.2022 KW - Automation KW - High-throughput KW - Machine learning KW - Materials informatics KW - Bonding Analysis PY - 2022 AN - OPUS4-56417 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -