Filtern
Dokumenttyp
- Vortrag (7) (entfernen)
Referierte Publikation
- nein (7) (entfernen)
Schlagworte
- Bonding Analysis (7) (entfernen)
Organisationseinheit der BAM
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.
Bonds and local atomic environments are crucial descriptors of material properties. They have been used to create design rules and heuristics for materials. More and more frequently, they are used as features in machine learning. Implementations and algorithms (e.g., ChemEnv and LobsterEnv) for identifying these 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 and for machine-learning approaches. The latter relates to a general trend toward automation in density functional-based materials science. The lecture will demonstrate how our tools, that assess local atomic environments, helped to test and develop heuristics and design rules and an intuitive understanding of materials.
Talk about my recent research on data-driven chemical understanding with geometrical and quantum-chemical bonding analysis.
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.[5,6] Furthermore, the general trend toward automation in density functional-based materials science and some of our recent contributions will be discussed.
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.