Data-driven chemical understanding with geometrical and quantum-chemical bonding analysis
Dr. Janine George
Bundesanstalt für Materialforschung und -prüfung, Abteilung Materialchemie, Nachwuchsgruppe "Computergestütztes Materialdesign" and Friedrich-Schiller-Universität Jena, Institut für Festkörpertheorie und Optik
Chemical bonding and coordination environments are crucial descriptors of material properties. They have previously been applied to creating chemical design guidelines and chemical heuristics.[1] They are currently being used as features in machine learning more and more frequently.[2] I will discuss implementations and algorithms (ChemEnv and LobsterEnv) for identifying these coordination environments based on geometrical characteristics and chemical bond quantum chemical analysis.[3–5] I will demonstrate how these techniques helped in testing chemical heuristics like the Pauling rule and thereby improved our understanding of chemistry.[6] I will also show how these tools can be used to create new design guidelines and a new understanding of chemistry.[4,7] To use quantum-chemical bonding analysis on a large-scale and for machine-learning approaches, fully automatic workflows and analysis tools have been developed.[4,8] 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.[9]
References
[1] J. George, G. Hautier, Trends in Chemistry 2021, 3, 86–95.
[2] A. M. Ganose, A. Jain, MRS Commun. 2019, 9, 874–881. [3] D. Waroquiers, J. George, M. Horton, S. Schenk, K. A. Persson, G.-M. Rignanese, X. Gonze, G. Hautier, Acta Cryst B 2020, 76, 683–695.
[4] J. George, G. Petretto, A. Naik, M. Esters, A. J. Jackson, R. Nelson, R. Dronskowski, G.-M. Rignanese, G. Hautier, ChemPlusChem 2022, e202200123, DOI: 10.1002/cplu.202200123.
[5] R. Nelson, C. Ertural, J. George, V. L. Deringer, G. Hautier, R. Dronskowski, J. Comput. Chem 2020, 41, 1931–1940.
[6] J. George, D. Waroquiers, D. Di Stefano, G. Petretto, G. Rignanese, G. Hautier, Angew. Chem. Int. Ed. 2020, 59, 7569–7575.
[7] W. Chen, J. George, J. B. Varley, G.-M. Rignanese, G. Hautier, Npj Comput. Mater. 2019, 5, 72.
[8] “LobsterPy,” can be found under github.com/JaGeo/LobsterPy, 2022.
[9] J. George, Trends Chem. 2021, 3, 697–699.