Data-driven chemical understanding and machine learning of materials properties Janine George 1,2 1 Federal Institute for Materials Research and Testing, Department Materials Chemistry, Junior Group „Computational Materials Design“, Germany 2 Friedrich Schiller University Jena, Institute Condensed Matter Theory and Optics, Germany Bonds and local atomic environments are crucial descriptors of material properties. They have been used to create design rules and heuristics for materials. [1] More and more frequently, they are used as features in machine learning. [2,3] 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. [4,5] Fully automatic workflows and analysis tools have been developed to use quantum-chemical bonding analysis on a large scale and for machine-learning approaches. [5,6] The latter relates to a general trend toward automation in density functional-based materials science. [7] 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. [5,8-11] References 1. J. George, G. Hautier, Trends Chem. 2021, 3, 86–95. 2. A. M. Ganose, A. Jain, MRS Commun. 2019, 9, 874–881. 3. J. George, G. Hautier, A. P. Bartók, G. Csányi, V. L. Deringer, J. Chem. Phys. 2020, 153, 044104. 4. 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. 5. 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. 6. “LobsterPy,” can be found under https://github.com/JaGeo/LobsterPy, 2022. 7. J. George, Trends Chem. 2021, 3, 697–699. 8. W. Chen, J. George, J. B. Varley, G.-M. Rignanese, G. Hautier, Npj Comput. Mater. 2019, 5, 72. 9. D. Dahliah, G. Brunin, J. George, V.-A. Ha, G.-M. Rignanese, G. Hautier, Energy Environ. Sci. 2021, 14, 5057–5073. 10. A. Naik, C. Ertural, N. Dhamrait, N. Benner, J. George, In Preparation 2023. 11. P. Benner, J. George, In Preparation 2023.
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