TY - CONF A1 - George, Janine T1 - Automated quantum-chemical bonding analysis with workflow tools 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, other recent workflow contributions to the Materials Project software infrastructure (pymatgen, atomate2) related to phonons and machine-learning potentials will be discussed. T2 - ADIS Workshop 2023 CY - Tegernsee, Germany DA - 29.10.2023 KW - Automation KW - Materials Properties KW - DFT KW - Workflows KW - Machine Learning KW - Thermodynamic Properties PY - 2023 AN - OPUS4-58745 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -