Janine George

Bundesanstalt für Materialforschung und -prüfung, FSU Jena

Automated quantum-chemical bonding analysis with workflow tools

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.[1–3] 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.[4,5] Fully automatic workflows and analysis tools have been developed to use quantum-chemical bonding analysis on a large scale.[5,6] 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.[7] Furthermore, other recent workflow contributions to the Materials Project software infrastructure (pymatgen, atomate2) related to phonons and machine-learning potentials will be discussed.[8–10]

AM Ganose, A Jain 
Robocrystallographer: automated crystal structure text descriptions and analysis
MRS Communications 9 ,  874 - 881 (2019)
J. George, G. Hautier, A. P. Bartók, G. Csányi, V. L. Deringer
Combining phonon accuracy with high transferability in Gaussian approximation potential models
J. Chem. Phys. 2020, 153, 044104.
J. George, G. Hautier
Chemist versus machine: Traditional knowledge versus machine learning techniques
Trends Chem. 2021, 3, 86–95.
D. Waroquiers, J. George, M. Horton, S. Schenk, K. A. Persson, G.-M. Rignanese, X. Gonze, G. Hautier
ChemEnv: a fast and robust coordination environment identification tool
Acta Cryst B 2020, 76, 683–695.
J. George, G. Petretto, A. Naik, M. Esters, A. J. Jackson, R. Nelson, R. Dronskowski, G.-M. Rignanese, G. Hautier
Automated Bonding Analysis with Crystal Orbital Hamilton Populations
ChemPlusChem 2022, 87, e202200123.
J. George et al.
can be found under
A. A. Naik, C. Ertural, N. Dhamrait, P. Benner, J. George
A Quantum-Chemical Bonding Database for Solid-State Materials
Sci Data 2023, 10, 610.
J. George,
Automation in DFT-based computational materials science
Trends Chem. 2021, 3, 697–699.
A. Ganose, et al.,
can be found under
S. P. Ong, W. D. Richards, A. Jain, G. Hautier, M. Kocher, S. Cholia, D. Gunter, V. L. Chevrier, K. A. Persson, G. Ceder,.
Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis
Comput. Mater. Sci. 2013, 68, 314–319
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