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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 will demonstrate how these techniques helped in testing chemical heuristics like the Pauling rule and thereby improved our understanding of chemistry. I will 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.
In this talk, the importance of metadata is underscored by real-world examples.
Metadata is essential to alleviating the reproducibility crises in science. This imples that a wide range of metadata must be collected, with a heavy emphasis on the automated collection of such metadata. This must subsequently be organized in an intelligible, archival structure, when possible with units and uncertainties.
Such metadata can aid in improving the usage efficiency of instrumentation, as is demonstrated on the MOUSE instrument. This metadata can now be used to connect the various aspects of the holistic experimental procedure to gain better insights on the materials structure.
A second example shows the extraction and organization of such metadata from an automated materials development platform, collected during the synthesis of 1200 samples. These metadata from the synthesis can then be linked to the results from the analysis of these samples, to find direct correlations between the synthesis parameters and the final structure of the materials.
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.
The talk „Automation in computational materials science“ deals with the current state of automation in the field of computational materials science. It illustrates how automation can, for example, be used to speed up the search for new ferroelectric materials and spintronic materials. Furthermore, it lists current tools for automation and challenges in the field.