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BAM Junior research group „Computational materials design“

Source: Janine George

Many renewable energy applications require the development of tailored and improved materials. The research group led by Professor Janine George focuses on data analysis, machine learning and high-throughput calculations for the search and design of new materials, thus complementing the scientific portfolio of the Materials Chemistry department. In addition to her work as head of the junior research group, Janine George was appointed Professor of Materials Informatics at Friedrich Schiller University Jena in September 2023. Due to her outstanding performance, she was also honored in December 2023 as one of four young scientists of the Werner-von-Siemens-Ring Foundation.

Team

The group consists of experts in ab initio calculations, chemical bonding analysis and vibrational properties. Dr. George recently presented the group's research in a seminar as part of the Materials Project seminar series.

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Research focus

New chemical heuristics

The group works to test and develop chemical heuristics (intuitive rules) to advance knowledge and understanding in solid-state chemistry and physics,1,2 using geometric and quantum chemical descriptors for chemical environments. For the latter, the group is developing open-source tools for automated bond analysis.3–5

High-throughput-calculations and automations

For the development of new chemical heuristics, it is necessary to have large amounts of reliably calculated material data. Here, the group develops workflows and methods for high-throughput calculations to reliably perform such calculations.3,5 Among other things, the group regularly contributes with its own developments to well-known open-source material informatics codes such as Pymatgen, Atomate or Atomate2 and has a lot of expertise with these codes.

Vibrational Properties

Vibration properties play a fundamental role in the stability and in heat transport of materials. Both material properties are particularly important for the safe use of materials. Here, the group is concerned with the ab initio prediction of such data in order to advance machine learning of such properties.6–8

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Methods and workflows

The team is developing the following methods and workflows:

  • Ab initio simulations for materials and molecules
  • Ab initio calculation of vibrational properties of crystalline materials
  • Material design for functional materials
  • Workflow and automation development of ab initio calculations
  • Development of geometric and quantum-chemical bonding-based material descriptors
  • Data analysis and machine learning of material properties

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References

(1) George, J.; Waroquiers, D.; Di Stefano, D.; Petretto, G.; Rignanese, G.; Hautier, G. The Limited Predictive Power of the Pauling Rules. Angew. Chem. Int. Ed. 2020, 59 (19), 7569–7575.
https://onlinelibrary.wiley.com/doi/10.1002/anie.202000829.
(2) George, J.; Hautier, G. Chemist versus Machine: Traditional Knowledge versus Machine Learning Techniques. Trends Chem. 2021, 3 (2), 86–95.
https://doi.org/10.1016/j.trechm.2020.10.007.
(3) George, J. Automation in DFT-Based Computational Materials Science. Trends Chem. 2021, 3 (9), 697–699.
https://doi.org/10.1016/j.trechm.2021.07.001.
(4) LobsterPy. LobsterPY.
https://github.com/JaGeo/LobsterPy.
(5) George, J.; Petretto, G.; Naik, A.; Esters, M.; Jackson, A. J.; Nelson, R.; Dronskowski, R.; Rignanese, G.-M.; Hautier, G. Automated Bonding Analysis with Crystal Orbital Hamilton Populations. ChemPlusChem 2022, e202200123, DOI: 10.1002/cplu.202200123.
https://doi.org/10.1002/cplu.202200123.
(6) George, J.; Hautier, G.; Bartók, A. P.; Csányi, G.; Deringer, V. L. Combining Phonon Accuracy with High Transferability in Gaussian Approximation Potential Models. J. Chem. Phys. 2020, 153 (4), 044104.
https://doi.org/10.1063/5.0013826.
(7) Hanus, R.; George, J.; Wood, M.; Bonkowski, A.; Cheng, Y.; Abernathy, D. L.; Manley, M. E.; Hautier, G.; Snyder, G. J.; Hermann, R. P. Uncovering Design Principles for Amorphous-like Heat Conduction Using Two-Channel Lattice Dynamics. Materials Today Physics 2021, 100344.
https://doi.org/10.1016/j.mtphys.2021.100344.
(8) Bernges, T.; Hanus, R.; Wankmiller, B.; Imasato, K.; Lin, S.; Ghidiu, M.; Gerlitz, M.; Peterlechner, M.; Graham, S.; Hautier, G.; Pei, Y.; Hansen, M. R.; Wilde, G.; Snyder, G. J.; George, J.; Agne, M. T.; Zeier, W. G. Considering the Role of Ion Transport in Diffuson-Dominated Thermal Conductivity. Advanced Energy Materials 2022, 12, 2200717.
https://doi.org/10.1002/aenm.202200717.

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