TY - CONF A1 - George, Janine T1 - Data-Driven Chemical Understanding with Bonding Analysis N2 - Bonds and local atomic environments are crucial descriptors of material properties. They have been used to create design rules and heuristics and as features in machine learning of materials properties. Implementations and algorithms (e.g., ChemEnv and LobsterEnv) for identifying local atomic environments based on geometrical characteristics and quantum-chemical bonding analysis are nowadays available. Fully automatic workflows and analysis tools have been developed to use quantum-chemical bonding analysis on a large scale. The lecture will demonstrate how our tools, that assess local atomic environments and perform automatic bonding analysis, help to develop new machine learning models and a new intuitive understanding of materials. Furthermore, the general trend toward automation in density functional-based materials science and some of our recent contributions will be discussed. T2 - Otaniemi Center for Atomic-scale Materials Modeling Seminar CY - Aalto, Finland DA - 11.03.2024 KW - Automation KW - Materials Design KW - Bonding Analysis KW - Machine Learning PY - 2024 AN - OPUS4-59671 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -