Ingenieurwissenschaften und zugeordnete Tätigkeiten
Filtern
Erscheinungsjahr
- 2022 (18) (entfernen)
Dokumenttyp
- Vortrag (8)
- Zeitschriftenartikel (4)
- Forschungsdatensatz (4)
- Posterpräsentation (2)
Schlagworte
- Automation (11)
- DFT (6)
- High-throughput (4)
- Bonding Analysis (3)
- Bonding analysis (3)
- High-throughput computations (3)
- Phonons (3)
- Chemical bonds (2)
- Machine learning (2)
- Materialinformatik (2)
- Structure prediction (2)
- Workflows (2)
- Automatisierung (1)
- Chemical Understanding (1)
- Chemical heuristics (1)
- Cheminformatics (1)
- Computerchemie (1)
- Crystal Orbital Hamilton Populations (1)
- Density functional theory (1)
- Diffusons (1)
- Materialdesign (1)
- Materials Informatics (1)
- Materials informatics (1)
- PU Learning (1)
- Synthesizability (1)
- Thermoelectrics (1)
Organisationseinheit der BAM
- 6 Materialchemie (18)
- 6.0 Abteilungsleitung und andere (18)
- VP Vizepräsident (1)
- VP.1 eScience (1)
Eingeladener Vortrag
- nein (8)
Automated bonding analysis software has been developed based on Crystal Orbital Hamilton Populations to facilitate high-throughput bonding analysis and machine-learning of bonding features. This work presents the software and discusses its applications to simple and complex materials such as GaN, NaCl, the oxynitrides XTaO2N (X=Ca, Ba, Sr) and Yb14Mn1Sb11.
Invited for this month’s cover are researchers from Bundesanstalt für Materialforschung und -prüfung (Federal Institute for Materials Research and Testing) in Germany, Friedrich Schiller University Jena, Université catholique de Louvain, University of Oregon, Science & Technology Facilities Council, RWTH Aachen University, Hoffmann Institute of Advanced Materials, and Dartmouth College. The cover picture shows a workflow for automatic bonding analysis with Python tools (green python). The bonding analysis itself is performed with the program LOBSTER (red lobster). The starting point is a crystal structure, and the results are automatic assessments of the bonding situation based on Crystal Orbital Hamilton Populations (COHP), including automatic plots and text outputs. Coordination environments and charges are also assessed. More information can be found in the Research Article by J. George, G. Hautier, and co-workers.
Chemical heuristics are essential to understanding molecules and materials in chemistry. The periodic table, atomic radii, and electronegativities are only a few examples. Initially, they have been developed by a combination of physical insight and a limited amount of data. It is now possible to test these heuristics and generate new ones using automation based on Materials Informatic tools like pymatgen and greater amounts of data from databases such as a Materials Project. In this session, I'll speak about heuristics and design rules based on coordination environments and the concept of chemical bonding. For example, we have tested the Pauling rules which describe the stability of materials based on coordination environments and their connections on 5000 oxides from the Materials Project. In addition, we have created automated processes for analyzing the chemical bonding situation in crystalline materials with Lobster (www.cohp.de) in order to discover new heuristics and design rules.
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.